{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/text-generation/papers/ran/6","list_of":"/task/text-generation","task":"Text Generation","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":6,"pages_in_order":7,"rows_per_page":100,"rows":[501,600],"of":610,"counts":{"archive_papers_tagged":5335,"with_a_code_link":2047,"where_syntology_ran_a_sample":610,"not_listed_spam_title":0,"listed":5335,"listed_where_code_ran":610,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":503,"every_run_a_failure_of_syntologys_instrument":107,"listed_with_a_run_with_no_instrument_failure":503,"listed_every_run_a_failure_of_syntologys_instrument":107,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/text-generation/papers/ran/1","prev":"/task/text-generation/papers/ran/5","next":"/task/text-generation/papers/ran/7","papers":[{"url":"/paper/generating-formulaic-text-by-splicing","slug":"generating-formulaic-text-by-splicing","title":"Data-to-text Generation by Splicing Together Nearest Neighbors","date":"2021-01-20","arxiv_id":"2101.08248","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":1,"n_ran_checked":3,"n_instrument":6,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":1,"n_pointer_only":10,"phrase":"9 ran (of which 1 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 0 violated, 1 with no contract checked; 6 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/generating-formulaic-text-by-splicing#ran","syntology_url":"https://syntology.ai/paper/2101.08248","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.08248"}},"official":{"repos":["swiseman/neighbor-splicing"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":1,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/what-makes-good-in-context-examples-for-gpt-3","slug":"what-makes-good-in-context-examples-for-gpt-3","title":"What Makes Good In-Context Examples for GPT-$3$?","date":"2021-01-17","arxiv_id":"2101.06804","repositories_listed":3,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/what-makes-good-in-context-examples-for-gpt-3#ran","syntology_url":"https://syntology.ai/paper/2101.06804","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.06804"}},"official":null}},{"url":"/paper/textbox-a-unified-modularized-and-extensible","slug":"textbox-a-unified-modularized-and-extensible","title":"TextBox: A Unified, Modularized, and Extensible Framework for Text Generation","date":"2021-01-06","arxiv_id":"2101.02046","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/textbox-a-unified-modularized-and-extensible#ran","syntology_url":"https://syntology.ai/paper/2101.02046","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.02046"}},"official":{"repos":["RUCAIBox/TextBox"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/prefix-tuning-optimizing-continuous-prompts","slug":"prefix-tuning-optimizing-continuous-prompts","title":"Prefix-Tuning: Optimizing Continuous Prompts for Generation","date":"2021-01-01","arxiv_id":"2101.00190","repositories_listed":13,"syntology":{"n":6,"n_ran":4,"n_constructed":2,"n_ran_checked":2,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"4 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/prefix-tuning-optimizing-continuous-prompts#ran","syntology_url":"https://syntology.ai/paper/2101.00190","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.00190"}},"official":{"repos":["XiangLi1999/PrefixTuning"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/directed-beam-search-plug-and-play-lexically","slug":"directed-beam-search-plug-and-play-lexically","title":"Directed Beam Search: Plug-and-Play Lexically Constrained Language Generation","date":"2020-12-31","arxiv_id":"2012.15416","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/directed-beam-search-plug-and-play-lexically#ran","syntology_url":"https://syntology.ai/paper/2012.15416","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.15416"}},"official":{"repos":["dapascual/DirectedBeamSearch"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-theoretical-analysis-of-the-repetition","slug":"a-theoretical-analysis-of-the-repetition","title":"A Theoretical Analysis of the Repetition Problem in Text Generation","date":"2020-12-29","arxiv_id":"2012.14660","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":2,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/a-theoretical-analysis-of-the-repetition#ran","syntology_url":"https://syntology.ai/paper/2012.14660","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.14660"}},"official":{"repos":["fuzihaofzh/repetition-problem-nlg"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/2012-11635","slug":"2012-11635","title":"A Distributional Approach to Controlled Text Generation","date":"2020-12-21","arxiv_id":"2012.11635","repositories_listed":2,"syntology":{"n":8,"n_ran":4,"n_constructed":1,"n_ran_checked":1,"n_instrument":3,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":8,"phrase":"4 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/2012-11635#ran","syntology_url":"https://syntology.ai/paper/2012.11635","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.11635"}},"official":{"repos":["naver/gdc"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/adapting-a-language-model-for-controlled","slug":"adapting-a-language-model-for-controlled","title":"Adapting a Language Model for Controlled Affective Text Generation","date":"2020-11-08","arxiv_id":"2011.04000","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/adapting-a-language-model-for-controlled#ran","syntology_url":"https://syntology.ai/paper/2011.04000","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.04000"}},"official":{"repos":["ishikasingh/Affective-text-gen"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/improving-variational-autoencoder-for-text","slug":"improving-variational-autoencoder-for-text","title":"Improving Variational Autoencoder for Text Modelling with Timestep-Wise Regularisation","date":"2020-11-02","arxiv_id":"2011.01136","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/improving-variational-autoencoder-for-text#ran","syntology_url":"https://syntology.ai/paper/2011.01136","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.01136"}},"official":{"repos":["ruizheliUOA/TWR-VAE"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/generating-radiology-reports-via-memory","slug":"generating-radiology-reports-via-memory","title":"Generating Radiology Reports via Memory-driven Transformer","date":"2020-10-30","arxiv_id":"2010.16056","repositories_listed":2,"syntology":{"n":31,"n_ran":26,"n_constructed":13,"n_ran_checked":20,"n_instrument":6,"n_unverified":5,"n_honours":3,"n_violates":3,"n_no_contract":14,"n_pointer_only":25,"phrase":"26 ran (of which 13 constructed an object rather than computing a result; 20 with no instrument failure: 3 honoured, 3 violated, 14 with no contract checked; 6 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/generating-radiology-reports-via-memory#ran","syntology_url":"https://syntology.ai/paper/2010.16056","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.16056"}},"official":{"repos":["zhjohnchan/R2Gen"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text","listed","official"]}}},{"url":"/paper/improving-factual-completeness-and","slug":"improving-factual-completeness-and","title":"Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation","date":"2020-10-20","arxiv_id":"2010.10042","repositories_listed":3,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/improving-factual-completeness-and#ran","syntology_url":"https://syntology.ai/paper/2010.10042","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.10042"}},"official":{"repos":["ysmiura/ifcc"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/substance-over-style-document-level-targeted","slug":"substance-over-style-document-level-targeted","title":"Substance over Style: Document-Level Targeted Content Transfer","date":"2020-10-16","arxiv_id":"2010.08618","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/substance-over-style-document-level-targeted#ran","syntology_url":"https://syntology.ai/paper/2010.08618","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.08618"}},"official":{"repos":["microsoft/document-level-targeted-content-transfer"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text"]}}},{"url":"/paper/incorporating-bert-into-parallel-sequence","slug":"incorporating-bert-into-parallel-sequence","title":"Incorporating BERT into Parallel Sequence Decoding with Adapters","date":"2020-10-13","arxiv_id":"2010.06138","repositories_listed":1,"syntology":{"n":13,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":5,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":3,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/incorporating-bert-into-parallel-sequence#ran","syntology_url":"https://syntology.ai/paper/2010.06138","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.06138"}},"official":{"repos":["lemmonation/abnet"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/back-to-the-future-unsupervised-backprop","slug":"back-to-the-future-unsupervised-backprop","title":"Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning","date":"2020-10-12","arxiv_id":"2010.05906","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":0,"n_instrument":5,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":8,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 5 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/back-to-the-future-unsupervised-backprop#ran","syntology_url":"https://syntology.ai/paper/2010.05906","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.05906"}},"official":{"repos":["qkaren/unsup_gen_for_cms_reasoning"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/on-long-tailed-phenomena-in-neural-machine","slug":"on-long-tailed-phenomena-in-neural-machine","title":"On Long-Tailed Phenomena in Neural Machine Translation","date":"2020-10-10","arxiv_id":"2010.04924","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/on-long-tailed-phenomena-in-neural-machine#ran","syntology_url":"https://syntology.ai/paper/2010.04924","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.04924"}},"official":{"repos":["vyraun/long-tailed"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/lifelong-language-knowledge-distillation","slug":"lifelong-language-knowledge-distillation","title":"Lifelong Language Knowledge Distillation","date":"2020-10-05","arxiv_id":"2010.02123","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/lifelong-language-knowledge-distillation#ran","syntology_url":"https://syntology.ai/paper/2010.02123","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.02123"}},"official":{"repos":["voidism/L2KD"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/utterance-level-dialogue-understanding-an","slug":"utterance-level-dialogue-understanding-an","title":"Utterance-level Dialogue Understanding: An Empirical Study","date":"2020-09-29","arxiv_id":"2009.13902","repositories_listed":2,"syntology":{"n":11,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/utterance-level-dialogue-understanding-an#ran","syntology_url":"https://syntology.ai/paper/2009.13902","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.13902"}},"official":{"repos":["declare-lab/dialogue-understanding"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/inltk-natural-language-toolkit-for-indic","slug":"inltk-natural-language-toolkit-for-indic","title":"iNLTK: Natural Language Toolkit for Indic Languages","date":"2020-09-26","arxiv_id":"2009.12534","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/inltk-natural-language-toolkit-for-indic#ran","syntology_url":"https://syntology.ai/paper/2009.12534","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.12534"}},"official":{"repos":["goru001/inltk"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/kg-bart-knowledge-graph-augmented-bart-for","slug":"kg-bart-knowledge-graph-augmented-bart-for","title":"KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning","date":"2020-09-26","arxiv_id":"2009.12677","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/kg-bart-knowledge-graph-augmented-bart-for#ran","syntology_url":"https://syntology.ai/paper/2009.12677","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.12677"}},"official":{"repos":["yeliu918/KG-BART"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/realtoxicityprompts-evaluating-neural-toxic","slug":"realtoxicityprompts-evaluating-neural-toxic","title":"RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models","date":"2020-09-24","arxiv_id":"2009.11462","repositories_listed":3,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/realtoxicityprompts-evaluating-neural-toxic#ran","syntology_url":"https://syntology.ai/paper/2009.11462","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.11462"}},"official":{"repos":["allenai/real-toxicity-prompts"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/language-generation-with-multi-hop-reasoning","slug":"language-generation-with-multi-hop-reasoning","title":"Language Generation with Multi-Hop Reasoning on Commonsense Knowledge Graph","date":"2020-09-24","arxiv_id":"2009.11692","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/language-generation-with-multi-hop-reasoning#ran","syntology_url":"https://syntology.ai/paper/2009.11692","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.11692"}},"official":{"repos":["cdjhz/multigen"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/text-generation-by-learning-from-off-policy","slug":"text-generation-by-learning-from-off-policy","title":"Text Generation by Learning from Demonstrations","date":"2020-09-16","arxiv_id":"2009.07839","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":2,"n_ran_checked":3,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/text-generation-by-learning-from-off-policy#ran","syntology_url":"https://syntology.ai/paper/2009.07839","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.07839"}},"official":{"repos":["yzpang/gold-off-policy-text-gen-iclr21"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text"]}}},{"url":"/paper/adversarial-watermarking-transformer-towards","slug":"adversarial-watermarking-transformer-towards","title":"Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data Hiding","date":"2020-09-07","arxiv_id":"2009.03015","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/adversarial-watermarking-transformer-towards#ran","syntology_url":"https://syntology.ai/paper/2009.03015","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.03015"}},"official":null}},{"url":"/paper/the-language-interpretability-tool-extensible","slug":"the-language-interpretability-tool-extensible","title":"The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models","date":"2020-08-12","arxiv_id":"2008.05122","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/the-language-interpretability-tool-extensible#ran","syntology_url":"https://syntology.ai/paper/2008.05122","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.05122"}},"official":{"repos":["PAIR-code/lit"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/investigating-pretrained-language-models-for","slug":"investigating-pretrained-language-models-for","title":"Investigating Pretrained Language Models for Graph-to-Text Generation","date":"2020-07-16","arxiv_id":"2007.08426","repositories_listed":3,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":2,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/investigating-pretrained-language-models-for#ran","syntology_url":"https://syntology.ai/paper/2007.08426","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.08426"}},"official":{"repos":["UKPLab/plms-graph2text"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/dart-open-domain-structured-data-record-to","slug":"dart-open-domain-structured-data-record-to","title":"DART: Open-Domain Structured Data Record to Text Generation","date":"2020-07-06","arxiv_id":"2007.02871","repositories_listed":2,"syntology":{"n":14,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/dart-open-domain-structured-data-record-to#ran","syntology_url":"https://syntology.ai/paper/2007.02871","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.02871"}},"official":{"repos":["Yale-LILY/dart"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/learning-sparse-prototypes-for-text","slug":"learning-sparse-prototypes-for-text","title":"Learning Sparse Prototypes for Text Generation","date":"2020-06-29","arxiv_id":"2006.16336","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":3,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":1,"phrase":"8 ran (of which 3 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-sparse-prototypes-for-text#ran","syntology_url":"https://syntology.ai/paper/2006.16336","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.16336"}},"official":{"repos":["jxhe/sparse-text-prototype"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":3,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/unsupervised-evaluation-of-interactive-dialog","slug":"unsupervised-evaluation-of-interactive-dialog","title":"Unsupervised Evaluation of Interactive Dialog with DialoGPT","date":"2020-06-23","arxiv_id":"2006.12719","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unsupervised-evaluation-of-interactive-dialog#ran","syntology_url":"https://syntology.ai/paper/2006.12719","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.12719"}},"official":{"repos":["shikib/fed"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/evidence-aware-inferential-text-generation","slug":"evidence-aware-inferential-text-generation","title":"Evidence-Aware Inferential Text Generation with Vector Quantised Variational AutoEncoder","date":"2020-06-15","arxiv_id":"2006.08101","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/evidence-aware-inferential-text-generation#ran","syntology_url":"https://syntology.ai/paper/2006.08101","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.08101"}},"official":{"repos":["microsoft/EA-VQ-VAE"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-latent-space-energy-based-prior","slug":"learning-latent-space-energy-based-prior","title":"Learning Latent Space Energy-Based Prior Model","date":"2020-06-15","arxiv_id":"2006.08205","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":4,"n_ran_checked":5,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"6 ran (of which 4 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/learning-latent-space-energy-based-prior#ran","syntology_url":"https://syntology.ai/paper/2006.08205","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.08205"}},"official":null}},{"url":"/paper/improving-gan-training-with-probability-ratio","slug":"improving-gan-training-with-probability-ratio","title":"Improving GAN Training with Probability Ratio Clipping and Sample Reweighting","date":"2020-06-12","arxiv_id":"2006.06900","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/improving-gan-training-with-probability-ratio#ran","syntology_url":"https://syntology.ai/paper/2006.06900","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.06900"}},"official":{"repos":["Holmeswww/PPOGAN"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/cocon-a-self-supervised-approach-for","slug":"cocon-a-self-supervised-approach-for","title":"CoCon: A Self-Supervised Approach for Controlled Text Generation","date":"2020-06-05","arxiv_id":"2006.03535","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":3,"n_ran_checked":4,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"5 ran (of which 3 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/cocon-a-self-supervised-approach-for#ran","syntology_url":"https://syntology.ai/paper/2006.03535","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.03535"}},"official":{"repos":["alvinchangw/COCON_ICLR2021"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/cascaded-text-generation-with-markov","slug":"cascaded-text-generation-with-markov","title":"Cascaded Text Generation with Markov Transformers","date":"2020-06-01","arxiv_id":"2006.01112","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/cascaded-text-generation-with-markov#ran","syntology_url":"https://syntology.ai/paper/2006.01112","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.01112"}},"official":{"repos":["harvardnlp/cascaded-generation"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/massive-choice-ample-tasks-machamp-a-toolkit","slug":"massive-choice-ample-tasks-machamp-a-toolkit","title":"Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP","date":"2020-05-29","arxiv_id":"2005.14672","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/massive-choice-ample-tasks-machamp-a-toolkit#ran","syntology_url":"https://syntology.ai/paper/2005.14672","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.14672"}},"official":{"repos":["machamp-nlp/machamp"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/retrieval-augmented-generation-for-knowledge","slug":"retrieval-augmented-generation-for-knowledge","title":"Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks","date":"2020-05-22","arxiv_id":"2005.11401","repositories_listed":18,"syntology":{"n":6,"n_ran":6,"n_constructed":3,"n_ran_checked":5,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"6 ran (of which 3 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/retrieval-augmented-generation-for-knowledge#ran","syntology_url":"https://syntology.ai/paper/2005.11401","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.11401"}},"official":null}},{"url":"/paper/gpt-too-a-language-model-first-approach-for","slug":"gpt-too-a-language-model-first-approach-for","title":"GPT-too: A language-model-first approach for AMR-to-text generation","date":"2020-05-18","arxiv_id":"2005.09123","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":2,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/gpt-too-a-language-model-first-approach-for#ran","syntology_url":"https://syntology.ai/paper/2005.09123","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.09123"}},"official":{"repos":["IBM/GPT-too-AMR2text"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/posterior-control-of-blackbox-generation","slug":"posterior-control-of-blackbox-generation","title":"Posterior Control of Blackbox Generation","date":"2020-05-10","arxiv_id":"2005.04560","repositories_listed":2,"syntology":{"n":6,"n_ran":4,"n_constructed":3,"n_ran_checked":3,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":6,"phrase":"4 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/posterior-control-of-blackbox-generation#ran","syntology_url":"https://syntology.ai/paper/2005.04560","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.04560"}},"official":{"repos":["XiangLi1999/PosteriorControl-NLG"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/transformer-based-end-to-end-question","slug":"transformer-based-end-to-end-question","title":"Simplifying Paragraph-level Question Generation via Transformer Language Models","date":"2020-05-03","arxiv_id":"2005.01107","repositories_listed":4,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/transformer-based-end-to-end-question#ran","syntology_url":"https://syntology.ai/paper/2005.01107","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.01107"}},"official":null}},{"url":"/paper/on-faithfulness-and-factuality-in-abstractive","slug":"on-faithfulness-and-factuality-in-abstractive","title":"On Faithfulness and Factuality in Abstractive Summarization","date":"2020-05-02","arxiv_id":"2005.00661","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/on-faithfulness-and-factuality-in-abstractive#ran","syntology_url":"https://syntology.ai/paper/2005.00661","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.00661"}},"official":{"repos":["google-research-datasets/xsum_hallucination_annotations"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text"]}}},{"url":"/paper/synthesizer-rethinking-self-attention-in","slug":"synthesizer-rethinking-self-attention-in","title":"Synthesizer: Rethinking Self-Attention in Transformer Models","date":"2020-05-02","arxiv_id":"2005.00743","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/synthesizer-rethinking-self-attention-in#ran","syntology_url":"https://syntology.ai/paper/2005.00743","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.00743"}},"official":null}},{"url":"/paper/pointer-constrained-text-generation-via","slug":"pointer-constrained-text-generation-via","title":"POINTER: Constrained Progressive Text Generation via Insertion-based Generative Pre-training","date":"2020-05-01","arxiv_id":"2005.00558","repositories_listed":1,"syntology":{"n":18,"n_ran":11,"n_constructed":0,"n_ran_checked":6,"n_instrument":5,"n_unverified":7,"n_honours":1,"n_violates":1,"n_no_contract":4,"n_pointer_only":1,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 1 violated, 4 with no contract checked; 5 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/pointer-constrained-text-generation-via#ran","syntology_url":"https://syntology.ai/paper/2005.00558","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.00558"}},"official":{"repos":["dreasysnail/POINTER"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/control-generate-augment-a-scalable-framework","slug":"control-generate-augment-a-scalable-framework","title":"Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text Generation","date":"2020-04-30","arxiv_id":"2004.14983","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/control-generate-augment-a-scalable-framework#ran","syntology_url":"https://syntology.ai/paper/2004.14983","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.14983"}},"official":null}},{"url":"/paper/probabilistically-masked-language-model","slug":"probabilistically-masked-language-model","title":"Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order","date":"2020-04-24","arxiv_id":"2004.11579","repositories_listed":3,"syntology":{"n":35,"n_ran":21,"n_constructed":1,"n_ran_checked":12,"n_instrument":9,"n_unverified":14,"n_honours":4,"n_violates":3,"n_no_contract":5,"n_pointer_only":29,"phrase":"21 ran (of which 1 constructed an object rather than computing a result; 12 with no instrument failure: 4 honoured, 3 violated, 5 with no contract checked; 9 where Syntology's instrument failed) · 14 unverified","sample_list":"/paper/probabilistically-masked-language-model#ran","syntology_url":"https://syntology.ai/paper/2004.11579","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.11579"}},"official":{"repos":["huawei-noah/Pretrained-Language-Model"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":1,"n_ran_no_instrument_failure":8,"n_unverified":13,"ran_from_kinds":["listed","official","unlocated"]}}},{"url":"/paper/logical-natural-language-generation-from-open","slug":"logical-natural-language-generation-from-open","title":"Logical Natural Language Generation from Open-Domain Tables","date":"2020-04-22","arxiv_id":"2004.10404","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/logical-natural-language-generation-from-open#ran","syntology_url":"https://syntology.ai/paper/2004.10404","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.10404"}},"official":{"repos":["wenhuchen/LogicNLG"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/rigid-formats-controlled-text-generation","slug":"rigid-formats-controlled-text-generation","title":"SongNet: Rigid Formats Controlled Text Generation","date":"2020-04-17","arxiv_id":"2004.08022","repositories_listed":2,"syntology":{"n":8,"n_ran":7,"n_constructed":1,"n_ran_checked":6,"n_instrument":1,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"7 ran (of which 1 constructed an object rather than computing a result; 6 with no instrument failure: 2 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/rigid-formats-controlled-text-generation#ran","syntology_url":"https://syntology.ai/paper/2004.08022","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.08022"}},"official":{"repos":["lipiji/SongNet"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/bleurt-learning-robust-metrics-for-text","slug":"bleurt-learning-robust-metrics-for-text","title":"BLEURT: Learning Robust Metrics for Text Generation","date":"2020-04-09","arxiv_id":"2004.04696","repositories_listed":4,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/bleurt-learning-robust-metrics-for-text#ran","syntology_url":"https://syntology.ai/paper/2004.04696","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.04696"}},"official":{"repos":["google-research/bleurt"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/generating-narrative-text-in-a-switching","slug":"generating-narrative-text-in-a-switching","title":"Generating Narrative Text in a Switching Dynamical System","date":"2020-04-08","arxiv_id":"2004.03762","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/generating-narrative-text-in-a-switching#ran","syntology_url":"https://syntology.ai/paper/2004.03762","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.03762"}},"official":{"repos":["StonyBrookNLP/SLDS-Stories"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/exploring-versatile-generative-language-model","slug":"exploring-versatile-generative-language-model","title":"Exploring Versatile Generative Language Model Via Parameter-Efficient Transfer Learning","date":"2020-04-08","arxiv_id":"2004.03829","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":2,"n_instrument":6,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 6 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/exploring-versatile-generative-language-model#ran","syntology_url":"https://syntology.ai/paper/2004.03829","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.03829"}},"official":{"repos":["zlinao/VGLM"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/tldr-token-loss-dynamic-reweighting-for","slug":"tldr-token-loss-dynamic-reweighting-for","title":"TLDR: Token Loss Dynamic Reweighting for Reducing Repetitive Utterance Generation","date":"2020-03-26","arxiv_id":"2003.11963","repositories_listed":1,"syntology":{"n":15,"n_ran":15,"n_constructed":0,"n_ran_checked":14,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":15,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/tldr-token-loss-dynamic-reweighting-for#ran","syntology_url":"https://syntology.ai/paper/2003.11963","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.11963"}},"official":{"repos":["ShaojieJiang/tldr"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/scrabblegan-semi-supervised-varying-length","slug":"scrabblegan-semi-supervised-varying-length","title":"ScrabbleGAN: Semi-Supervised Varying Length Handwritten Text Generation","date":"2020-03-23","arxiv_id":"2003.10557","repositories_listed":3,"syntology":{"n":15,"n_ran":14,"n_constructed":0,"n_ran_checked":12,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":3,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/scrabblegan-semi-supervised-varying-length#ran","syntology_url":"https://syntology.ai/paper/2003.10557","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.10557"}},"official":null}},{"url":"/paper/bridging-text-and-video-a-universal","slug":"bridging-text-and-video-a-universal","title":"Bridging Text and Video: A Universal Multimodal Transformer for Video-Audio Scene-Aware Dialog","date":"2020-02-01","arxiv_id":"2002.00163","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/bridging-text-and-video-a-universal#ran","syntology_url":"https://syntology.ai/paper/2002.00163","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.00163"}},"official":null}},{"url":"/paper/paraphrase-generation-with-latent-bag-of-1","slug":"paraphrase-generation-with-latent-bag-of-1","title":"Paraphrase Generation with Latent Bag of Words","date":"2020-01-07","arxiv_id":"2001.01941","repositories_listed":2,"syntology":{"n":12,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/paraphrase-generation-with-latent-bag-of-1#ran","syntology_url":"https://syntology.ai/paper/2001.01941","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2001.01941"}},"official":{"repos":["FranxYao/Deep-Generative-Models-for-Natural-Language-Processing","FranxYao/dgm_latent_bow"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/m2-meshed-memory-transformer-for-image","slug":"m2-meshed-memory-transformer-for-image","title":"Meshed-Memory Transformer for Image Captioning","date":"2019-12-17","arxiv_id":"1912.08226","repositories_listed":2,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":3,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/m2-meshed-memory-transformer-for-image#ran","syntology_url":"https://syntology.ai/paper/1912.08226","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.08226"}},"official":{"repos":["aimagelab/meshed-memory-transformer"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/plug-and-play-language-models-a-simple","slug":"plug-and-play-language-models-a-simple","title":"Plug and Play Language Models: A Simple Approach to Controlled Text Generation","date":"2019-12-04","arxiv_id":"1912.02164","repositories_listed":7,"syntology":{"n":22,"n_ran":17,"n_constructed":0,"n_ran_checked":15,"n_instrument":2,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":2,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/plug-and-play-language-models-a-simple#ran","syntology_url":"https://syntology.ai/paper/1912.02164","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.02164"}},"official":{"repos":["uber-research/PPLM"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/automatically-neutralizing-subjective-bias-in","slug":"automatically-neutralizing-subjective-bias-in","title":"Automatically Neutralizing Subjective Bias in Text","date":"2019-11-21","arxiv_id":"1911.09709","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/automatically-neutralizing-subjective-bias-in#ran","syntology_url":"https://syntology.ai/paper/1911.09709","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.09709"}},"official":{"repos":["rpryzant/neutralizing-bias"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-transformer-for-graph-to-sequence","slug":"graph-transformer-for-graph-to-sequence","title":"Graph Transformer for Graph-to-Sequence Learning","date":"2019-11-18","arxiv_id":"1911.07470","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/graph-transformer-for-graph-to-sequence#ran","syntology_url":"https://syntology.ai/paper/1911.07470","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.07470"}},"official":{"repos":["jcyk/gtos"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/distilling-the-knowledge-of-bert-for-text-1","slug":"distilling-the-knowledge-of-bert-for-text-1","title":"Distilling Knowledge Learned in BERT for Text Generation","date":"2019-11-10","arxiv_id":"1911.03829","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/distilling-the-knowledge-of-bert-for-text-1#ran","syntology_url":"https://syntology.ai/paper/1911.03829","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.03829"}},"official":{"repos":["ChenRocks/Distill-BERT-Textgen"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/commongen-a-constrained-text-generation","slug":"commongen-a-constrained-text-generation","title":"CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning","date":"2019-11-09","arxiv_id":"1911.03705","repositories_listed":3,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/commongen-a-constrained-text-generation#ran","syntology_url":"https://syntology.ai/paper/1911.03705","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.03705"}},"official":null}},{"url":"/paper/human-and-automatic-detection-of-generated","slug":"human-and-automatic-detection-of-generated","title":"Automatic Detection of Generated Text is Easiest when Humans are Fooled","date":"2019-11-02","arxiv_id":"1911.00650","repositories_listed":2,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/human-and-automatic-detection-of-generated#ran","syntology_url":"https://syntology.ai/paper/1911.00650","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.00650"}},"official":null}},{"url":"/paper/bart-denoising-sequence-to-sequence-pre","slug":"bart-denoising-sequence-to-sequence-pre","title":"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension","date":"2019-10-29","arxiv_id":"1910.13461","repositories_listed":47,"syntology":{"n":53,"n_ran":40,"n_constructed":7,"n_ran_checked":31,"n_instrument":9,"n_unverified":13,"n_honours":1,"n_violates":1,"n_no_contract":29,"n_pointer_only":13,"phrase":"40 ran (of which 7 constructed an object rather than computing a result; 31 with no instrument failure: 1 honoured, 1 violated, 29 with no contract checked; 9 where Syntology's instrument failed) · 13 unverified","sample_list":"/paper/bart-denoising-sequence-to-sequence-pre#ran","syntology_url":"https://syntology.ai/paper/1910.13461","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.13461"}},"official":null}},{"url":"/paper/transformers-state-of-the-art-natural","slug":"transformers-state-of-the-art-natural","title":"HuggingFace's Transformers: State-of-the-art Natural Language Processing","date":"2019-10-09","arxiv_id":"1910.03771","repositories_listed":9,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/transformers-state-of-the-art-natural#ran","syntology_url":"https://syntology.ai/paper/1910.03771","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.03771"}},"official":{"repos":["huggingface/transformers"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/natural-to-formal-language-generation-using-1","slug":"natural-to-formal-language-generation-using-1","title":"Mapping Natural-language Problems to Formal-language Solutions Using Structured Neural Representations","date":"2019-10-05","arxiv_id":"1910.02339","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/natural-to-formal-language-generation-using-1#ran","syntology_url":"https://syntology.ai/paper/1910.02339","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.02339"}},"official":null}},{"url":"/paper/unified-vision-language-pre-training-for","slug":"unified-vision-language-pre-training-for","title":"Unified Vision-Language Pre-Training for Image Captioning and VQA","date":"2019-09-24","arxiv_id":"1909.11059","repositories_listed":3,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":8,"n_instrument":3,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":14,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/unified-vision-language-pre-training-for#ran","syntology_url":"https://syntology.ai/paper/1909.11059","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.11059"}},"official":{"repos":["LuoweiZhou/VLP"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/moss-end-to-end-dialog-system-framework-with","slug":"moss-end-to-end-dialog-system-framework-with","title":"MOSS: End-to-End Dialog System Framework with Modular Supervision","date":"2019-09-12","arxiv_id":"1909.05528","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":2,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 2 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/moss-end-to-end-dialog-system-framework-with#ran","syntology_url":"https://syntology.ai/paper/1909.05528","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.05528"}},"official":null}},{"url":"/paper/ctrl-a-conditional-transformer-language-model-1","slug":"ctrl-a-conditional-transformer-language-model-1","title":"CTRL: A Conditional Transformer Language Model for Controllable Generation","date":"2019-09-11","arxiv_id":"1909.05858","repositories_listed":8,"syntology":{"n":14,"n_ran":14,"n_constructed":3,"n_ran_checked":11,"n_instrument":3,"n_unverified":0,"n_honours":3,"n_violates":1,"n_no_contract":7,"n_pointer_only":0,"phrase":"14 ran (of which 3 constructed an object rather than computing a result; 11 with no instrument failure: 3 honoured, 1 violated, 7 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ctrl-a-conditional-transformer-language-model-1#ran","syntology_url":"https://syntology.ai/paper/1909.05858","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.05858"}},"official":null}},{"url":"/paper/counterfactual-story-reasoning-and-generation","slug":"counterfactual-story-reasoning-and-generation","title":"Counterfactual Story Reasoning and Generation","date":"2019-09-09","arxiv_id":"1909.04076","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/counterfactual-story-reasoning-and-generation#ran","syntology_url":"https://syntology.ai/paper/1909.04076","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.04076"}},"official":{"repos":["qkaren/Counterfactual-StoryRW"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/moverscore-text-generation-evaluating-with","slug":"moverscore-text-generation-evaluating-with","title":"MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance","date":"2019-09-05","arxiv_id":"1909.02622","repositories_listed":4,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/moverscore-text-generation-evaluating-with#ran","syntology_url":"https://syntology.ai/paper/1909.02622","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.02622"}},"official":null}},{"url":"/paper/data-driven-approach-to-encoding-and-decoding","slug":"data-driven-approach-to-encoding-and-decoding","title":"Data-Driven Approach to Encoding and Decoding 3-D Crystal Structures","date":"2019-09-03","arxiv_id":"1909.00949","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/data-driven-approach-to-encoding-and-decoding#ran","syntology_url":"https://syntology.ai/paper/1909.00949","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.00949"}},"official":null}},{"url":"/paper/encode-tag-realize-high-precision-text","slug":"encode-tag-realize-high-precision-text","title":"Encode, Tag, Realize: High-Precision Text Editing","date":"2019-09-03","arxiv_id":"1909.01187","repositories_listed":5,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/encode-tag-realize-high-precision-text#ran","syntology_url":"https://syntology.ai/paper/1909.01187","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.01187"}},"official":{"repos":["google-research/lasertagger"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/neural-text-generation-with-unlikelihood","slug":"neural-text-generation-with-unlikelihood","title":"Neural Text Generation with Unlikelihood Training","date":"2019-08-12","arxiv_id":"1908.04319","repositories_listed":6,"syntology":{"n":6,"n_ran":6,"n_constructed":1,"n_ran_checked":2,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"phrase":"6 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/neural-text-generation-with-unlikelihood#ran","syntology_url":"https://syntology.ai/paper/1908.04319","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.04319"}},"official":{"repos":["facebookresearch/unlikelihood_training"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/generating-sentences-from-disentangled","slug":"generating-sentences-from-disentangled","title":"Generating Sentences from Disentangled Syntactic and Semantic Spaces","date":"2019-07-06","arxiv_id":"1907.05789","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/generating-sentences-from-disentangled#ran","syntology_url":"https://syntology.ai/paper/1907.05789","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.05789"}},"official":{"repos":["baoy-nlp/DSS-VAE"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/gltr-statistical-detection-and-visualization","slug":"gltr-statistical-detection-and-visualization","title":"GLTR: Statistical Detection and Visualization of Generated Text","date":"2019-06-10","arxiv_id":"1906.04043","repositories_listed":6,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/gltr-statistical-detection-and-visualization#ran","syntology_url":"https://syntology.ai/paper/1906.04043","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.04043"}},"official":{"repos":["HendrikStrobelt/detecting-fake-text"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/data-to-text-generation-with-entity-modeling","slug":"data-to-text-generation-with-entity-modeling","title":"Data-to-text Generation with Entity Modeling","date":"2019-06-07","arxiv_id":"1906.03221","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/data-to-text-generation-with-entity-modeling#ran","syntology_url":"https://syntology.ai/paper/1906.03221","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.03221"}},"official":{"repos":["ratishsp/data2text-entity-py"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/handling-divergent-reference-texts-when","slug":"handling-divergent-reference-texts-when","title":"Handling Divergent Reference Texts when Evaluating Table-to-Text Generation","date":"2019-06-03","arxiv_id":"1906.01081","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/handling-divergent-reference-texts-when#ran","syntology_url":"https://syntology.ai/paper/1906.01081","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.01081"}},"official":null}},{"url":"/paper/defending-against-neural-fake-news","slug":"defending-against-neural-fake-news","title":"Defending Against Neural Fake News","date":"2019-05-29","arxiv_id":"1905.12616","repositories_listed":4,"syntology":{"n":10,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":10,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/defending-against-neural-fake-news#ran","syntology_url":"https://syntology.ai/paper/1905.12616","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.12616"}},"official":{"repos":["rowanz/grover"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/bertscore-evaluating-text-generation-with","slug":"bertscore-evaluating-text-generation-with","title":"BERTScore: Evaluating Text Generation with BERT","date":"2019-04-21","arxiv_id":"1904.09675","repositories_listed":20,"syntology":{"n":76,"n_ran":44,"n_constructed":4,"n_ran_checked":10,"n_instrument":34,"n_unverified":32,"n_honours":3,"n_violates":2,"n_no_contract":5,"n_pointer_only":33,"phrase":"44 ran (of which 4 constructed an object rather than computing a result; 10 with no instrument failure: 3 honoured, 2 violated, 5 with no contract checked; 34 where Syntology's instrument failed) · 32 unverified","sample_list":"/paper/bertscore-evaluating-text-generation-with#ran","syntology_url":"https://syntology.ai/paper/1904.09675","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.09675"}},"official":{"repos":["Tiiiger/bert_score"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":4,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/positional-encoding-to-control-output","slug":"positional-encoding-to-control-output","title":"Positional Encoding to Control Output Sequence Length","date":"2019-04-16","arxiv_id":"1904.07418","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/positional-encoding-to-control-output#ran","syntology_url":"https://syntology.ai/paper/1904.07418","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.07418"}},"official":{"repos":["takase/control-length"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/jointly-measuring-diversity-and-quality-in","slug":"jointly-measuring-diversity-and-quality-in","title":"Jointly Measuring Diversity and Quality in Text Generation Models","date":"2019-04-08","arxiv_id":"1904.03971","repositories_listed":3,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/jointly-measuring-diversity-and-quality-in#ran","syntology_url":"https://syntology.ai/paper/1904.03971","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.03971"}},"official":{"repos":["IAmS4n/TextGenerationEvaluationMetrics"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/step-by-step-separating-planning-from","slug":"step-by-step-separating-planning-from","title":"Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation","date":"2019-04-06","arxiv_id":"1904.03396","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/step-by-step-separating-planning-from#ran","syntology_url":"https://syntology.ai/paper/1904.03396","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.03396"}},"official":{"repos":["AmitMY/chimera"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/text-generation-from-knowledge-graphs-with","slug":"text-generation-from-knowledge-graphs-with","title":"Text Generation from Knowledge Graphs with Graph Transformers","date":"2019-04-04","arxiv_id":"1904.02342","repositories_listed":3,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/text-generation-from-knowledge-graphs-with#ran","syntology_url":"https://syntology.ai/paper/1904.02342","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.02342"}},"official":{"repos":["rikdz/GraphWriter"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/riemannian-normalizing-flow-on-variational","slug":"riemannian-normalizing-flow-on-variational","title":"Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling","date":"2019-04-04","arxiv_id":"1904.02399","repositories_listed":1,"syntology":{"n":14,"n_ran":13,"n_constructed":0,"n_ran_checked":12,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":1,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/riemannian-normalizing-flow-on-variational#ran","syntology_url":"https://syntology.ai/paper/1904.02399","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.02399"}},"official":{"repos":["kingofspace0wzz/wae-rnf-lm"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/fairseq-a-fast-extensible-toolkit-for","slug":"fairseq-a-fast-extensible-toolkit-for","title":"fairseq: A Fast, Extensible Toolkit for Sequence Modeling","date":"2019-04-01","arxiv_id":"1904.01038","repositories_listed":6,"syntology":{"n":11,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/fairseq-a-fast-extensible-toolkit-for#ran","syntology_url":"https://syntology.ai/paper/1904.01038","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.01038"}},"official":{"repos":["pytorch/fairseq"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/pretraining-based-natural-language-generation","slug":"pretraining-based-natural-language-generation","title":"Pretraining-Based Natural Language Generation for Text Summarization","date":"2019-02-25","arxiv_id":"1902.09243","repositories_listed":4,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pretraining-based-natural-language-generation#ran","syntology_url":"https://syntology.ai/paper/1902.09243","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.09243"}},"official":null}},{"url":"/paper/lagging-inference-networks-and-posterior","slug":"lagging-inference-networks-and-posterior","title":"Lagging Inference Networks and Posterior Collapse in Variational Autoencoders","date":"2019-01-16","arxiv_id":"1901.05534","repositories_listed":2,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/lagging-inference-networks-and-posterior#ran","syntology_url":"https://syntology.ai/paper/1901.05534","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.05534"}},"official":{"repos":["jxhe/vae-lagging-encoder"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/text-infilling","slug":"text-infilling","title":"Text Infilling","date":"2019-01-01","arxiv_id":"1901.00158","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/text-infilling#ran","syntology_url":"https://syntology.ai/paper/1901.00158","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.00158"}},"official":{"repos":["VegB/Text_Infilling"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/evaluating-text-gans-as-language-models","slug":"evaluating-text-gans-as-language-models","title":"Evaluating Text GANs as Language Models","date":"2018-10-30","arxiv_id":"1810.12686","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/evaluating-text-gans-as-language-models#ran","syntology_url":"https://syntology.ai/paper/1810.12686","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.12686"}},"official":{"repos":["GuyTevet/SeqGAN-eval"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-graph-convolutional-encoders-for","slug":"deep-graph-convolutional-encoders-for","title":"Deep Graph Convolutional Encoders for Structured Data to Text Generation","date":"2018-10-23","arxiv_id":"1810.09995","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/deep-graph-convolutional-encoders-for#ran","syntology_url":"https://syntology.ai/paper/1810.09995","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.09995"}},"official":{"repos":["diegma/graph-2-text"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/adversarial-text-generation-via-feature","slug":"adversarial-text-generation-via-feature","title":"Adversarial Text Generation via Feature-Mover's Distance","date":"2018-09-17","arxiv_id":"1809.06297","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adversarial-text-generation-via-feature#ran","syntology_url":"https://syntology.ai/paper/1809.06297","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.06297"}},"official":null}},{"url":"/paper/sql-to-text-generation-with-graph-to-sequence","slug":"sql-to-text-generation-with-graph-to-sequence","title":"SQL-to-Text Generation with Graph-to-Sequence Model","date":"2018-09-14","arxiv_id":"1809.05255","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/sql-to-text-generation-with-graph-to-sequence#ran","syntology_url":"https://syntology.ai/paper/1809.05255","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.05255"}},"official":{"repos":["IBM/SQL-to-Text"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/describing-a-knowledge-base","slug":"describing-a-knowledge-base","title":"Describing a Knowledge Base","date":"2018-09-06","arxiv_id":"1809.01797","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":4,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":3,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/describing-a-knowledge-base#ran","syntology_url":"https://syntology.ai/paper/1809.01797","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.01797"}},"official":{"repos":["EagleW/Describing_a_Knowledge_Base"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/texar-a-modularized-versatile-and-extensible-1","slug":"texar-a-modularized-versatile-and-extensible-1","title":"Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation","date":"2018-09-04","arxiv_id":"1809.00794","repositories_listed":4,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/texar-a-modularized-versatile-and-extensible-1#ran","syntology_url":"https://syntology.ai/paper/1809.00794","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.00794"}},"official":{"repos":["asyml/texar"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/learning-neural-templates-for-text-generation","slug":"learning-neural-templates-for-text-generation","title":"Learning Neural Templates for Text Generation","date":"2018-08-30","arxiv_id":"1808.10122","repositories_listed":2,"syntology":{"n":12,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/learning-neural-templates-for-text-generation#ran","syntology_url":"https://syntology.ai/paper/1808.10122","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.10122"}},"official":{"repos":["harvardnlp/neural-template-gen"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/natural-language-generation-for-electronic","slug":"natural-language-generation-for-electronic","title":"Natural Language Generation for Electronic Health Records","date":"2018-06-01","arxiv_id":"1806.01353","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/natural-language-generation-for-electronic#ran","syntology_url":"https://syntology.ai/paper/1806.01353","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.01353"}},"official":{"repos":["scotthlee/nrc"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/zero-shot-dialog-generation-with-cross-domain","slug":"zero-shot-dialog-generation-with-cross-domain","title":"Zero-Shot Dialog Generation with Cross-Domain Latent Actions","date":"2018-05-13","arxiv_id":"1805.04803","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":3,"n_pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/zero-shot-dialog-generation-with-cross-domain#ran","syntology_url":"https://syntology.ai/paper/1805.04803","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.04803"}},"official":{"repos":["snakeztc/NeuralDialog-ZSDG","snakeztc/SimDial"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/unsupervised-discrete-sentence-representation","slug":"unsupervised-discrete-sentence-representation","title":"Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation","date":"2018-04-22","arxiv_id":"1804.08069","repositories_listed":2,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":4,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unsupervised-discrete-sentence-representation#ran","syntology_url":"https://syntology.ai/paper/1804.08069","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.08069"}},"official":{"repos":["snakeztc/NeuralDialog-LAED"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/unsupervised-natural-language-generation-with","slug":"unsupervised-natural-language-generation-with","title":"Unsupervised Natural Language Generation with Denoising Autoencoders","date":"2018-04-21","arxiv_id":"1804.07899","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unsupervised-natural-language-generation-with#ran","syntology_url":"https://syntology.ai/paper/1804.07899","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.07899"}},"official":null}},{"url":"/paper/graph2seq-graph-to-sequence-learning-with","slug":"graph2seq-graph-to-sequence-learning-with","title":"Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks","date":"2018-04-03","arxiv_id":"1804.00823","repositories_listed":4,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":4,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/graph2seq-graph-to-sequence-learning-with#ran","syntology_url":"https://syntology.ai/paper/1804.00823","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.00823"}},"official":{"repos":["IBM/Graph2Seq"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/dp-gan-diversity-promoting-generative","slug":"dp-gan-diversity-promoting-generative","title":"DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified Text","date":"2018-02-05","arxiv_id":"1802.01345","repositories_listed":3,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dp-gan-diversity-promoting-generative#ran","syntology_url":"https://syntology.ai/paper/1802.01345","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.01345"}},"official":{"repos":["lancopku/DPGAN"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/long-text-generation-via-adversarial-training","slug":"long-text-generation-via-adversarial-training","title":"Long Text Generation via Adversarial Training with Leaked Information","date":"2017-09-24","arxiv_id":"1709.08624","repositories_listed":6,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/long-text-generation-via-adversarial-training#ran","syntology_url":"https://syntology.ai/paper/1709.08624","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1709.08624"}},"official":{"repos":["CR-Gjx/LeakGAN"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/relevance-of-unsupervised-metrics-in-task","slug":"relevance-of-unsupervised-metrics-in-task","title":"Relevance of Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language Generation","date":"2017-06-29","arxiv_id":"1706.09799","repositories_listed":3,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/relevance-of-unsupervised-metrics-in-task#ran","syntology_url":"https://syntology.ai/paper/1706.09799","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1706.09799"}},"official":{"repos":["Maluuba/nlg-eval"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}}],"record_sha256":"b568e2378de82e95c3b99bfbaded84fefa9db014d0709db63d664c11ef52229b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}