{"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/semantic-parsing/papers/ran/1","list_of":"/task/semantic-parsing","task":"Semantic Parsing","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":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,96],"of":96,"counts":{"archive_papers_tagged":1202,"with_a_code_link":414,"where_syntology_ran_a_sample":96,"not_listed_spam_title":0,"listed":1202,"listed_where_code_ran":96,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":81,"every_run_a_failure_of_syntologys_instrument":15,"listed_with_a_run_with_no_instrument_failure":81,"listed_every_run_a_failure_of_syntologys_instrument":15,"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/semantic-parsing/papers/ran/1","prev":null,"next":null,"papers":[{"url":"/paper/llm-al-bridging-large-language-models-and","slug":"llm-al-bridging-large-language-models-and","title":"LLM+AL: Bridging Large Language Models and Action Languages for Complex Reasoning about Actions","date":"2025-01-01","arxiv_id":"2501.00830","repositories_listed":0,"syntology":{"n":12,"n_ran":7,"n_constructed":0,"n_ran_checked":0,"n_instrument":7,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":12,"phrase":"7 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; 7 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/llm-al-bridging-large-language-models-and#ran","syntology_url":"https://syntology.ai/paper/2501.00830","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.00830"}},"official":null}},{"url":"/paper/cross-lingual-back-parsing-utterance","slug":"cross-lingual-back-parsing-utterance","title":"Cross-lingual Back-Parsing: Utterance Synthesis from Meaning Representation for Zero-Resource Semantic Parsing","date":"2024-10-01","arxiv_id":"2410.00513","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":5,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":7,"phrase":"5 ran (of which 5 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) · 2 unverified; every one of the 5 samples that ran constructed an object rather than computing a result","sample_list":"/paper/cross-lingual-back-parsing-utterance#ran","syntology_url":"https://syntology.ai/paper/2410.00513","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.00513"}},"official":{"repos":["deokhk/cbp"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":5,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/beyond-alignment-blind-video-face-restoration","slug":"beyond-alignment-blind-video-face-restoration","title":"Beyond Alignment: Blind Video Face Restoration via Parsing-Guided Temporal-Coherent Transformer","date":"2024-04-21","arxiv_id":"2404.13640","repositories_listed":1,"syntology":{"n":19,"n_ran":17,"n_constructed":0,"n_ran_checked":7,"n_instrument":10,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":19,"phrase":"17 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; 10 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/beyond-alignment-blind-video-face-restoration#ran","syntology_url":"https://syntology.ai/paper/2404.13640","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.13640"}},"official":{"repos":["kepengxu/pgtformer"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/tabsqlify-enhancing-reasoning-capabilities-of","slug":"tabsqlify-enhancing-reasoning-capabilities-of","title":"TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table Decomposition","date":"2024-04-15","arxiv_id":"2404.10150","repositories_listed":2,"syntology":{"n":15,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":8,"n_honours":6,"n_violates":0,"n_no_contract":0,"n_pointer_only":15,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 6 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/tabsqlify-enhancing-reasoning-capabilities-of#ran","syntology_url":"https://syntology.ai/paper/2404.10150","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.10150"}},"official":{"repos":["mahadi-nahid/tabsqlify"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/interactive-kbqa-multi-turn-interactions-for","slug":"interactive-kbqa-multi-turn-interactions-for","title":"Interactive-KBQA: Multi-Turn Interactions for Knowledge Base Question Answering with Large Language Models","date":"2024-02-23","arxiv_id":"2402.15131","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":3,"n_ran_checked":3,"n_instrument":4,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":10,"phrase":"7 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; 4 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/interactive-kbqa-multi-turn-interactions-for#ran","syntology_url":"https://syntology.ai/paper/2402.15131","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.15131"}},"official":{"repos":["jimxionggm/interactive-kbqa"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/cabinet-content-relevance-based-noise","slug":"cabinet-content-relevance-based-noise","title":"CABINET: Content Relevance based Noise Reduction for Table Question Answering","date":"2024-02-02","arxiv_id":"2402.01155","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/cabinet-content-relevance-based-noise#ran","syntology_url":"https://syntology.ai/paper/2402.01155","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.01155"}},"official":{"repos":["sohanpatnaik106/cabinet_qa"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/chain-of-table-evolving-tables-in-the","slug":"chain-of-table-evolving-tables-in-the","title":"Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding","date":"2024-01-09","arxiv_id":"2401.04398","repositories_listed":2,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":0,"n_instrument":6,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"6 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; 6 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/chain-of-table-evolving-tables-in-the#ran","syntology_url":"https://syntology.ai/paper/2401.04398","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.04398"}},"official":null}},{"url":"/paper/rethinking-tabular-data-understanding-with","slug":"rethinking-tabular-data-understanding-with","title":"Rethinking Tabular Data Understanding with Large Language Models","date":"2023-12-27","arxiv_id":"2312.16702","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/rethinking-tabular-data-understanding-with#ran","syntology_url":"https://syntology.ai/paper/2312.16702","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.16702"}},"official":{"repos":["Leolty/tablellm"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/leveraging-code-to-improve-in-context","slug":"leveraging-code-to-improve-in-context","title":"Leveraging Code to Improve In-context Learning for Semantic Parsing","date":"2023-11-16","arxiv_id":"2311.09519","repositories_listed":1,"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":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) · 2 unverified","sample_list":"/paper/leveraging-code-to-improve-in-context#ran","syntology_url":"https://syntology.ai/paper/2311.09519","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.09519"}},"official":{"repos":["allenai/code-semparse"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/multistage-collaborative-knowledge","slug":"multistage-collaborative-knowledge","title":"Multistage Collaborative Knowledge Distillation from a Large Language Model for Semi-Supervised Sequence Generation","date":"2023-11-15","arxiv_id":"2311.08640","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":0,"n_honours":2,"n_violates":1,"n_no_contract":0,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/multistage-collaborative-knowledge#ran","syntology_url":"https://syntology.ai/paper/2311.08640","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.08640"}},"official":{"repos":["andotalao24/multistage-collaborative-knowledge-distillation"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/weakly-supervised-semantic-parsing-with-2","slug":"weakly-supervised-semantic-parsing-with-2","title":"Weakly Supervised Semantic Parsing with Execution-based Spurious Program Filtering","date":"2023-11-02","arxiv_id":"2311.01161","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/weakly-supervised-semantic-parsing-with-2#ran","syntology_url":"https://syntology.ai/paper/2311.01161","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.01161"}},"official":{"repos":["klee972/exec-filter"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/slog-a-structural-generalization-benchmark","slug":"slog-a-structural-generalization-benchmark","title":"SLOG: A Structural Generalization Benchmark for Semantic Parsing","date":"2023-10-23","arxiv_id":"2310.15040","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"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: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/slog-a-structural-generalization-benchmark#ran","syntology_url":"https://syntology.ai/paper/2310.15040","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.15040"}},"official":{"repos":["bingzhilee/slog"],"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/semantic-decomposition-of-question-and-sql","slug":"semantic-decomposition-of-question-and-sql","title":"Semantic Decomposition of Question and SQL for Text-to-SQL Parsing","date":"2023-10-20","arxiv_id":"2310.13575","repositories_listed":1,"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/semantic-decomposition-of-question-and-sql#ran","syntology_url":"https://syntology.ai/paper/2310.13575","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.13575"}},"official":{"repos":["bgunlp/qpl"],"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/chatkbqa-a-generate-then-retrieve-framework","slug":"chatkbqa-a-generate-then-retrieve-framework","title":"ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models","date":"2023-10-13","arxiv_id":"2310.08975","repositories_listed":1,"syntology":{"n":16,"n_ran":11,"n_constructed":0,"n_ran_checked":8,"n_instrument":3,"n_unverified":5,"n_honours":0,"n_violates":1,"n_no_contract":7,"n_pointer_only":4,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 1 violated, 7 with no contract checked; 3 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/chatkbqa-a-generate-then-retrieve-framework#ran","syntology_url":"https://syntology.ai/paper/2310.08975","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.08975"}},"official":{"repos":["lhrlab/chatkbqa"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/code-style-in-context-learning-for-knowledge","slug":"code-style-in-context-learning-for-knowledge","title":"Code-Style In-Context Learning for Knowledge-Based Question Answering","date":"2023-09-09","arxiv_id":"2309.04695","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/code-style-in-context-learning-for-knowledge#ran","syntology_url":"https://syntology.ai/paper/2309.04695","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.04695"}},"official":{"repos":["arthurizijar/kb-coder"],"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/holistic-exploration-on-universal","slug":"holistic-exploration-on-universal","title":"Holistic Exploration on Universal Decompositional Semantic Parsing: Architecture, Data Augmentation, and LLM Paradigm","date":"2023-07-25","arxiv_id":"2307.13424","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/holistic-exploration-on-universal#ran","syntology_url":"https://syntology.ai/paper/2307.13424","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.13424"}},"official":{"repos":["hexuandeng/hexp4uds"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/differentiable-tree-operations-promote","slug":"differentiable-tree-operations-promote","title":"Differentiable Tree Operations Promote Compositional Generalization","date":"2023-06-01","arxiv_id":"2306.00751","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":6,"n_instrument":3,"n_unverified":3,"n_honours":2,"n_violates":0,"n_no_contract":4,"n_pointer_only":12,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 2 honoured, 0 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/differentiable-tree-operations-promote#ran","syntology_url":"https://syntology.ai/paper/2306.00751","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.00751"}},"official":{"repos":["psoulos/dtm"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/grammar-prompting-for-domain-specific","slug":"grammar-prompting-for-domain-specific","title":"Grammar Prompting for Domain-Specific Language Generation with Large Language Models","date":"2023-05-30","arxiv_id":"2305.19234","repositories_listed":2,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/grammar-prompting-for-domain-specific#ran","syntology_url":"https://syntology.ai/paper/2305.19234","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.19234"}},"official":{"repos":["berlino/grammar-prompting"],"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/compositional-generalization-without-trees","slug":"compositional-generalization-without-trees","title":"Compositional Generalization without Trees using Multiset Tagging and Latent Permutations","date":"2023-05-26","arxiv_id":"2305.16954","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 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; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/compositional-generalization-without-trees#ran","syntology_url":"https://syntology.ai/paper/2305.16954","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.16954"}},"official":{"repos":["namednil/multiset-perm"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/generating-data-for-symbolic-language-with","slug":"generating-data-for-symbolic-language-with","title":"Generating Data for Symbolic Language with Large Language Models","date":"2023-05-23","arxiv_id":"2305.13917","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/generating-data-for-symbolic-language-with#ran","syntology_url":"https://syntology.ai/paper/2305.13917","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.13917"}},"official":{"repos":["hkunlp/symgen"],"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/xtreme-up-a-user-centric-scarce-data","slug":"xtreme-up-a-user-centric-scarce-data","title":"XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages","date":"2023-05-19","arxiv_id":"2305.11938","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/xtreme-up-a-user-centric-scarce-data#ran","syntology_url":"https://syntology.ai/paper/2305.11938","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.11938"}},"official":{"repos":["google-research/xtreme-up"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-to-simulate-natural-language","slug":"learning-to-simulate-natural-language","title":"Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing","date":"2023-05-14","arxiv_id":"2305.08195","repositories_listed":2,"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":3,"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/learning-to-simulate-natural-language#ran","syntology_url":"https://syntology.ai/paper/2305.08195","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.08195"}},"official":{"repos":["hyan5/learning_to_simulate_nl_feedback"],"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/can-llm-already-serve-as-a-database-interface","slug":"can-llm-already-serve-as-a-database-interface","title":"Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs","date":"2023-05-04","arxiv_id":"2305.03111","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":0,"n_instrument":6,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"phrase":"6 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; 6 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/can-llm-already-serve-as-a-database-interface#ran","syntology_url":"https://syntology.ai/paper/2305.03111","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.03111"}},"official":null}},{"url":"/paper/dpf-learning-dense-prediction-fields-with","slug":"dpf-learning-dense-prediction-fields-with","title":"DPF: Learning Dense Prediction Fields with Weak Supervision","date":"2023-03-29","arxiv_id":"2303.16890","repositories_listed":1,"syntology":{"n":34,"n_ran":23,"n_constructed":13,"n_ran_checked":13,"n_instrument":10,"n_unverified":11,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":34,"phrase":"23 ran (of which 13 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 10 where Syntology's instrument failed) · 11 unverified","sample_list":"/paper/dpf-learning-dense-prediction-fields-with#ran","syntology_url":"https://syntology.ai/paper/2303.16890","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.16890"}},"official":{"repos":["cxx226/dpf"],"state":"official (archive's flag): 22 ran","n_ran":22,"n_constructed":13,"n_ran_no_instrument_failure":13,"n_unverified":11,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/recogs-how-incidental-details-of-a-logical","slug":"recogs-how-incidental-details-of-a-logical","title":"ReCOGS: How Incidental Details of a Logical Form Overshadow an Evaluation of Semantic Interpretation","date":"2023-03-24","arxiv_id":"2303.13716","repositories_listed":1,"syntology":{"n":17,"n_ran":17,"n_constructed":0,"n_ran_checked":16,"n_instrument":1,"n_unverified":0,"n_honours":1,"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; 16 with no instrument failure: 1 honoured, 0 violated, 15 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/recogs-how-incidental-details-of-a-logical#ran","syntology_url":"https://syntology.ai/paper/2303.13716","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.13716"}},"official":{"repos":["frankaging/recogs"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":16,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/lever-learning-to-verify-language-to-code","slug":"lever-learning-to-verify-language-to-code","title":"LEVER: Learning to Verify Language-to-Code Generation with Execution","date":"2023-02-16","arxiv_id":"2302.08468","repositories_listed":1,"syntology":{"n":22,"n_ran":18,"n_constructed":0,"n_ran_checked":18,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":1,"n_no_contract":17,"n_pointer_only":1,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 18 with no instrument failure: 0 honoured, 1 violated, 17 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/lever-learning-to-verify-language-to-code#ran","syntology_url":"https://syntology.ai/paper/2302.08468","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.08468"}},"official":{"repos":["niansong1996/lever"],"state":"official (archive's flag): 18 ran","n_ran":18,"n_constructed":0,"n_ran_no_instrument_failure":18,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/compositional-exemplars-for-in-context","slug":"compositional-exemplars-for-in-context","title":"Compositional Exemplars for In-context Learning","date":"2023-02-11","arxiv_id":"2302.05698","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":1,"n_ran_checked":2,"n_instrument":3,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"5 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/compositional-exemplars-for-in-context#ran","syntology_url":"https://syntology.ai/paper/2302.05698","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.05698"}},"official":{"repos":["hkunlp/icl-ceil"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/towards-autoformalization-of-mathematics-and","slug":"towards-autoformalization-of-mathematics-and","title":"Towards Autoformalization of Mathematics and Code Correctness: Experiments with Elementary Proofs","date":"2023-01-05","arxiv_id":"2301.02195","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/towards-autoformalization-of-mathematics-and#ran","syntology_url":"https://syntology.ai/paper/2301.02195","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.02195"}},"official":{"repos":["gc974517/autoformalization"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-knowledge-intensive-text-to-sql","slug":"towards-knowledge-intensive-text-to-sql","title":"Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge","date":"2023-01-03","arxiv_id":"2301.01067","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/towards-knowledge-intensive-text-to-sql#ran","syntology_url":"https://syntology.ai/paper/2301.01067","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.01067"}},"official":{"repos":["microsoft/ContextualSP"],"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/calibrated-interpretation-confidence","slug":"calibrated-interpretation-confidence","title":"Calibrated Interpretation: Confidence Estimation in Semantic Parsing","date":"2022-11-14","arxiv_id":"2211.07443","repositories_listed":2,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/calibrated-interpretation-confidence#ran","syntology_url":"https://syntology.ai/paper/2211.07443","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.07443"}},"official":{"repos":["esteng/calibration_metric","esteng/calibration_miso"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/reastap-injecting-table-reasoning-skills","slug":"reastap-injecting-table-reasoning-skills","title":"ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples","date":"2022-10-22","arxiv_id":"2210.12374","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/reastap-injecting-table-reasoning-skills#ran","syntology_url":"https://syntology.ai/paper/2210.12374","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12374"}},"official":{"repos":["yale-lily/reastap"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/binding-language-models-in-symbolic-languages","slug":"binding-language-models-in-symbolic-languages","title":"Binding Language Models in Symbolic Languages","date":"2022-10-06","arxiv_id":"2210.02875","repositories_listed":4,"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":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) · 1 unverified","sample_list":"/paper/binding-language-models-in-symbolic-languages#ran","syntology_url":"https://syntology.ai/paper/2210.02875","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.02875"}},"official":{"repos":["hkunlp/binder"],"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/omnitab-pretraining-with-natural-and-1","slug":"omnitab-pretraining-with-natural-and-1","title":"OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering","date":"2022-07-08","arxiv_id":"2207.03637","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"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) · 1 unverified","sample_list":"/paper/omnitab-pretraining-with-natural-and-1#ran","syntology_url":"https://syntology.ai/paper/2207.03637","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.03637"}},"official":{"repos":["jzbjyb/omnitab"],"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/benchclamp-a-benchmark-for-evaluating","slug":"benchclamp-a-benchmark-for-evaluating","title":"BenchCLAMP: A Benchmark for Evaluating Language Models on Syntactic and Semantic Parsing","date":"2022-06-21","arxiv_id":"2206.10668","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"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, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/benchclamp-a-benchmark-for-evaluating#ran","syntology_url":"https://syntology.ai/paper/2206.10668","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.10668"}},"official":{"repos":["microsoft/semantic_parsing_with_constrained_lm"],"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/prompt-injection-parameterization-of-fixed","slug":"prompt-injection-parameterization-of-fixed","title":"Prompt Injection: Parameterization of Fixed Inputs","date":"2022-05-31","arxiv_id":"2206.11349","repositories_listed":4,"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/prompt-injection-parameterization-of-fixed#ran","syntology_url":"https://syntology.ai/paper/2206.11349","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.11349"}},"official":{"repos":["unbiarirang/prompt-injection"],"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/a-simple-and-unified-tagging-model-with","slug":"a-simple-and-unified-tagging-model-with","title":"TAGPRIME: A Unified Framework for Relational Structure Extraction","date":"2022-05-25","arxiv_id":"2205.12585","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":2,"n_ran_checked":4,"n_instrument":6,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":2,"n_pointer_only":11,"phrase":"10 ran (of which 2 constructed an object rather than computing a result; 4 with no instrument failure: 2 honoured, 0 violated, 2 with no contract checked; 6 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/a-simple-and-unified-tagging-model-with#ran","syntology_url":"https://syntology.ai/paper/2205.12585","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.12585"}},"official":null}},{"url":"/paper/seqzero-few-shot-compositional-semantic-1","slug":"seqzero-few-shot-compositional-semantic-1","title":"SeqZero: Few-shot Compositional Semantic Parsing with Sequential Prompts and Zero-shot Models","date":"2022-05-15","arxiv_id":"2205.07381","repositories_listed":1,"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/seqzero-few-shot-compositional-semantic-1#ran","syntology_url":"https://syntology.ai/paper/2205.07381","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.07381"}},"official":{"repos":["amzn/seqzero"],"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/rasat-integrating-relational-structures-into","slug":"rasat-integrating-relational-structures-into","title":"RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL","date":"2022-05-14","arxiv_id":"2205.06983","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"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) · 4 unverified","sample_list":"/paper/rasat-integrating-relational-structures-into#ran","syntology_url":"https://syntology.ai/paper/2205.06983","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.06983"}},"official":{"repos":["lumia-group/rasat"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/subs-subtree-substitution-for-compositional-1","slug":"subs-subtree-substitution-for-compositional-1","title":"SUBS: Subtree Substitution for Compositional Semantic Parsing","date":"2022-05-03","arxiv_id":"2205.01538","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/subs-subtree-substitution-for-compositional-1#ran","syntology_url":"https://syntology.ai/paper/2205.01538","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.01538"}},"official":{"repos":["gt-salt/subs"],"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/arcaneqa-dynamic-program-induction-and","slug":"arcaneqa-dynamic-program-induction-and","title":"ArcaneQA: Dynamic Program Induction and Contextualized Encoding for Knowledge Base Question Answering","date":"2022-04-17","arxiv_id":"2204.08109","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/arcaneqa-dynamic-program-induction-and#ran","syntology_url":"https://syntology.ai/paper/2204.08109","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.08109"}},"official":{"repos":["dki-lab/arcaneqa"],"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":["unlocated"]}}},{"url":"/paper/compositionality-as-lexical-symmetry","slug":"compositionality-as-lexical-symmetry","title":"Compositionality as Lexical Symmetry","date":"2022-01-30","arxiv_id":"2201.12926","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/compositionality-as-lexical-symmetry#ran","syntology_url":"https://syntology.ai/paper/2201.12926","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.12926"}},"official":{"repos":["ekinakyurek/lexsym"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/unifiedskg-unifying-and-multi-tasking","slug":"unifiedskg-unifying-and-multi-tasking","title":"UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models","date":"2022-01-16","arxiv_id":"2201.05966","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/unifiedskg-unifying-and-multi-tasking#ran","syntology_url":"https://syntology.ai/paper/2201.05966","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.05966"}},"official":{"repos":["hkunlp/unifiedskg"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/sadga-structure-aware-dual-graph-aggregation","slug":"sadga-structure-aware-dual-graph-aggregation","title":"SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL","date":"2021-11-01","arxiv_id":"2111.00653","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":7,"phrase":"5 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; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/sadga-structure-aware-dual-graph-aggregation#ran","syntology_url":"https://syntology.ai/paper/2111.00653","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.00653"}},"official":{"repos":["dmirlab-group/sadga"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/reasonbert-pre-trained-to-reason-with-distant","slug":"reasonbert-pre-trained-to-reason-with-distant","title":"ReasonBERT: Pre-trained to Reason with Distant Supervision","date":"2021-09-10","arxiv_id":"2109.04912","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":1,"n_no_contract":6,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 1 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/reasonbert-pre-trained-to-reason-with-distant#ran","syntology_url":"https://syntology.ai/paper/2109.04912","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.04912"}},"official":{"repos":["sunlab-osu/reasonbert"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/picard-parsing-incrementally-for-constrained","slug":"picard-parsing-incrementally-for-constrained","title":"PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models","date":"2021-09-10","arxiv_id":"2109.05093","repositories_listed":3,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/picard-parsing-incrementally-for-constrained#ran","syntology_url":"https://syntology.ai/paper/2109.05093","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.05093"}},"official":{"repos":["ElementAI/picard"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/text-to-sql-in-the-wild-a-naturally-occurring","slug":"text-to-sql-in-the-wild-a-naturally-occurring","title":"Text-to-SQL in the Wild: A Naturally-Occurring Dataset Based on Stack Exchange Data","date":"2021-06-09","arxiv_id":"2106.05006","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"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 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) · 3 unverified","sample_list":"/paper/text-to-sql-in-the-wild-a-naturally-occurring#ran","syntology_url":"https://syntology.ai/paper/2106.05006","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.05006"}},"official":{"repos":["hirupert/sede"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/lexicon-learning-for-few-shot-neural-sequence","slug":"lexicon-learning-for-few-shot-neural-sequence","title":"Lexicon Learning for Few-Shot Neural Sequence Modeling","date":"2021-06-07","arxiv_id":"2106.03993","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/lexicon-learning-for-few-shot-neural-sequence#ran","syntology_url":"https://syntology.ai/paper/2106.03993","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.03993"}},"official":{"repos":["ekinakyurek/lexical"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/structured-reordering-for-modeling-latent","slug":"structured-reordering-for-modeling-latent","title":"Structured Reordering for Modeling Latent Alignments in Sequence Transduction","date":"2021-06-06","arxiv_id":"2106.03257","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/structured-reordering-for-modeling-latent#ran","syntology_url":"https://syntology.ai/paper/2106.03257","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.03257"}},"official":{"repos":["berlino/tensor2struct-public"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/inter-gps-interpretable-geometry-problem","slug":"inter-gps-interpretable-geometry-problem","title":"Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning","date":"2021-05-10","arxiv_id":"2105.04165","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/inter-gps-interpretable-geometry-problem#ran","syntology_url":"https://syntology.ai/paper/2105.04165","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.04165"}},"official":{"repos":["lupantech/InterGPS"],"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/a-generative-symbolic-model-for-more-general","slug":"a-generative-symbolic-model-for-more-general","title":"Towards General Natural Language Understanding with Probabilistic Worldbuilding","date":"2021-05-06","arxiv_id":"2105.02486","repositories_listed":2,"syntology":{"n":14,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":1,"n_no_contract":7,"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, 1 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/a-generative-symbolic-model-for-more-general#ran","syntology_url":"https://syntology.ai/paper/2105.02486","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.02486"}},"official":{"repos":["asaparov/fictionalgeoqa","asaparov/pwl"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-semantic-person-image-generation-by","slug":"learning-semantic-person-image-generation-by","title":"Learning Semantic Person Image Generation by Region-Adaptive Normalization","date":"2021-04-14","arxiv_id":"2104.06650","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":6,"n_ran_checked":6,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":11,"phrase":"9 ran (of which 6 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/learning-semantic-person-image-generation-by#ran","syntology_url":"https://syntology.ai/paper/2104.06650","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.06650"}},"official":{"repos":["cszy98/SPGNet"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":6,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/3d-to-2d-distillation-for-indoor-scene","slug":"3d-to-2d-distillation-for-indoor-scene","title":"3D-to-2D Distillation for Indoor Scene Parsing","date":"2021-04-06","arxiv_id":"2104.02243","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":1,"n_ran_checked":1,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":7,"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) · 3 unverified","sample_list":"/paper/3d-to-2d-distillation-for-indoor-scene#ran","syntology_url":"https://syntology.ai/paper/2104.02243","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.02243"}},"official":{"repos":["liuzhengzhe/3D-to-2D-Distillation-for-Indoor-Scene-Parsing"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/differentiable-multi-granularity-human","slug":"differentiable-multi-granularity-human","title":"Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic Parsing","date":"2021-03-08","arxiv_id":"2103.04570","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/differentiable-multi-granularity-human#ran","syntology_url":"https://syntology.ai/paper/2103.04570","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.04570"}},"official":{"repos":["tfzhou/MG-HumanParsing"],"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/conversational-semantic-parsing-for-dialog","slug":"conversational-semantic-parsing-for-dialog","title":"Conversational Semantic Parsing for Dialog State Tracking","date":"2020-10-24","arxiv_id":"2010.12770","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/conversational-semantic-parsing-for-dialog#ran","syntology_url":"https://syntology.ai/paper/2010.12770","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.12770"}},"official":{"repos":["apple/ml-tree-dst"],"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/smbop-semi-autoregressive-bottom-up-semantic","slug":"smbop-semi-autoregressive-bottom-up-semantic","title":"SmBoP: Semi-autoregressive Bottom-up Semantic Parsing","date":"2020-10-23","arxiv_id":"2010.12412","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/smbop-semi-autoregressive-bottom-up-semantic#ran","syntology_url":"https://syntology.ai/paper/2010.12412","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.12412"}},"official":{"repos":["OhadRubin/SmBop"],"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/on-the-potential-of-lexico-logical-alignments","slug":"on-the-potential-of-lexico-logical-alignments","title":"On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queries","date":"2020-10-21","arxiv_id":"2010.11246","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/on-the-potential-of-lexico-logical-alignments#ran","syntology_url":"https://syntology.ai/paper/2010.11246","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.11246"}},"official":{"repos":["tzshi/squall"],"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/cogs-a-compositional-generalization-challenge","slug":"cogs-a-compositional-generalization-challenge","title":"COGS: A Compositional Generalization Challenge Based on Semantic Interpretation","date":"2020-10-12","arxiv_id":"2010.05465","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/cogs-a-compositional-generalization-challenge#ran","syntology_url":"https://syntology.ai/paper/2010.05465","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.05465"}},"official":{"repos":["najoungkim/COGS"],"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/improving-compositional-generalization-in","slug":"improving-compositional-generalization-in","title":"Improving Compositional Generalization in Semantic Parsing","date":"2020-10-12","arxiv_id":"2010.05647","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/improving-compositional-generalization-in#ran","syntology_url":"https://syntology.ai/paper/2010.05647","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.05647"}},"official":{"repos":["inbaroren/improving-compgen-in-semparse"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/autoqa-from-databases-to-qa-semantic-parsers","slug":"autoqa-from-databases-to-qa-semantic-parsers","title":"AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data","date":"2020-10-09","arxiv_id":"2010.04806","repositories_listed":3,"syntology":{"n":26,"n_ran":13,"n_constructed":2,"n_ran_checked":10,"n_instrument":3,"n_unverified":13,"n_honours":1,"n_violates":4,"n_no_contract":5,"n_pointer_only":26,"phrase":"13 ran (of which 2 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 4 violated, 5 with no contract checked; 3 where Syntology's instrument failed) · 13 unverified","sample_list":"/paper/autoqa-from-databases-to-qa-semantic-parsers#ran","syntology_url":"https://syntology.ai/paper/2010.04806","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.04806"}},"official":{"repos":["stanford-oval/genienlp","stanford-oval/genie-toolkit","stanford-oval/schema2qa"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":2,"n_ran_no_instrument_failure":10,"n_unverified":13,"ran_from_kinds":["official"]}}},{"url":"/paper/grappa-grammar-augmented-pre-training-for","slug":"grappa-grammar-augmented-pre-training-for","title":"GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing","date":"2020-09-29","arxiv_id":"2009.13845","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":0,"n_instrument":6,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":7,"phrase":"6 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; 6 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/grappa-grammar-augmented-pre-training-for#ran","syntology_url":"https://syntology.ai/paper/2009.13845","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.13845"}},"official":null}},{"url":"/paper/grounded-adaptation-for-zero-shot-executable","slug":"grounded-adaptation-for-zero-shot-executable","title":"Grounded Adaptation for Zero-shot Executable Semantic Parsing","date":"2020-09-16","arxiv_id":"2009.07396","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/grounded-adaptation-for-zero-shot-executable#ran","syntology_url":"https://syntology.ai/paper/2009.07396","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.07396"}},"official":null}},{"url":"/paper/fast-semantic-parsing-with-well-typedness","slug":"fast-semantic-parsing-with-well-typedness","title":"Fast semantic parsing with well-typedness guarantees","date":"2020-09-15","arxiv_id":"2009.07365","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":7,"n_ran_checked":7,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":10,"phrase":"9 ran (of which 7 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/fast-semantic-parsing-with-well-typedness#ran","syntology_url":"https://syntology.ai/paper/2009.07365","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.07365"}},"official":{"repos":["coli-saar/am-parser"],"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/identity-guided-human-semantic-parsing-for","slug":"identity-guided-human-semantic-parsing-for","title":"Identity-Guided Human Semantic Parsing for Person Re-Identification","date":"2020-07-27","arxiv_id":"2007.13467","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"6 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/identity-guided-human-semantic-parsing-for#ran","syntology_url":"https://syntology.ai/paper/2007.13467","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.13467"}},"official":{"repos":["CASIA-IVA-Lab/ISP-reID"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/kqa-pro-a-large-diagnostic-dataset-for","slug":"kqa-pro-a-large-diagnostic-dataset-for","title":"KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Base","date":"2020-07-08","arxiv_id":"2007.03875","repositories_listed":2,"syntology":{"n":10,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":9,"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) · 9 unverified","sample_list":"/paper/kqa-pro-a-large-diagnostic-dataset-for#ran","syntology_url":"https://syntology.ai/paper/2007.03875","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.03875"}},"official":{"repos":["shijx12/kqapro_baselines"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":9,"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/cityscapes-panoptic-parts-and-pascal-panoptic","slug":"cityscapes-panoptic-parts-and-pascal-panoptic","title":"Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for Scene Understanding","date":"2020-04-16","arxiv_id":"2004.07944","repositories_listed":4,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 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; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/cityscapes-panoptic-parts-and-pascal-panoptic#ran","syntology_url":"https://syntology.ai/paper/2004.07944","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.07944"}},"official":{"repos":["tue-mps/panoptic_parts"],"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/graph-to-tree-neural-networks-for-learning","slug":"graph-to-tree-neural-networks-for-learning","title":"Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem","date":"2020-04-07","arxiv_id":"2004.13781","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/graph-to-tree-neural-networks-for-learning#ran","syntology_url":"https://syntology.ai/paper/2004.13781","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.13781"}},"official":{"repos":["IBM/Graph2Tree"],"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/tapas-weakly-supervised-table-parsing-via-pre","slug":"tapas-weakly-supervised-table-parsing-via-pre","title":"TAPAS: Weakly Supervised Table Parsing via Pre-training","date":"2020-04-05","arxiv_id":"2004.02349","repositories_listed":8,"syntology":{"n":14,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":0,"phrase":"14 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; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/tapas-weakly-supervised-table-parsing-via-pre#ran","syntology_url":"https://syntology.ai/paper/2004.02349","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.02349"}},"official":{"repos":["google-research/tapas"],"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/sparqa-skeleton-based-semantic-parsing-for","slug":"sparqa-skeleton-based-semantic-parsing-for","title":"SPARQA: Skeleton-based Semantic Parsing for Complex Questions over Knowledge Bases","date":"2020-03-31","arxiv_id":"2003.13956","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/sparqa-skeleton-based-semantic-parsing-for#ran","syntology_url":"https://syntology.ai/paper/2003.13956","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.13956"}},"official":{"repos":["nju-websoft/SPARQA"],"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/measuring-compositional-generalization-a-1","slug":"measuring-compositional-generalization-a-1","title":"Measuring Compositional Generalization: A Comprehensive Method on Realistic Data","date":"2019-12-20","arxiv_id":"1912.09713","repositories_listed":3,"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/measuring-compositional-generalization-a-1#ran","syntology_url":"https://syntology.ai/paper/1912.09713","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.09713"}},"official":{"repos":["google-research/google-research"],"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/merging-weak-and-active-supervision-for","slug":"merging-weak-and-active-supervision-for","title":"Merging Weak and Active Supervision for Semantic Parsing","date":"2019-11-29","arxiv_id":"1911.12986","repositories_listed":1,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/merging-weak-and-active-supervision-for#ran","syntology_url":"https://syntology.ai/paper/1911.12986","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.12986"}},"official":{"repos":["niansong1996/wassp"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/treegen-a-tree-based-transformer-architecture","slug":"treegen-a-tree-based-transformer-architecture","title":"TreeGen: A Tree-Based Transformer Architecture for Code Generation","date":"2019-11-22","arxiv_id":"1911.09983","repositories_listed":2,"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":0,"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/treegen-a-tree-based-transformer-architecture#ran","syntology_url":"https://syntology.ai/paper/1911.09983","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.09983"}},"official":{"repos":["zysszy/TreeGen"],"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/rat-sql-relation-aware-schema-encoding-and-1","slug":"rat-sql-relation-aware-schema-encoding-and-1","title":"RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers","date":"2019-11-10","arxiv_id":"1911.04942","repositories_listed":4,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":4,"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) · 2 unverified","sample_list":"/paper/rat-sql-relation-aware-schema-encoding-and-1#ran","syntology_url":"https://syntology.ai/paper/1911.04942","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.04942"}},"official":{"repos":["Microsoft/rat-sql"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/exploring-the-limits-of-transfer-learning","slug":"exploring-the-limits-of-transfer-learning","title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","date":"2019-10-23","arxiv_id":"1910.10683","repositories_listed":57,"syntology":{"n":31,"n_ran":21,"n_constructed":0,"n_ran_checked":20,"n_instrument":1,"n_unverified":10,"n_honours":1,"n_violates":0,"n_no_contract":19,"n_pointer_only":0,"phrase":"21 ran (of which 0 constructed an object rather than computing a result; 20 with no instrument failure: 1 honoured, 0 violated, 19 with no contract checked; 1 where Syntology's instrument failed) · 10 unverified","sample_list":"/paper/exploring-the-limits-of-transfer-learning#ran","syntology_url":"https://syntology.ai/paper/1910.10683","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.10683"}},"official":null}},{"url":"/paper/model-based-interactive-semantic-parsing-a","slug":"model-based-interactive-semantic-parsing-a","title":"Model-based Interactive Semantic Parsing: A Unified Framework and A Text-to-SQL Case Study","date":"2019-10-11","arxiv_id":"1910.05389","repositories_listed":2,"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/model-based-interactive-semantic-parsing-a#ran","syntology_url":"https://syntology.ai/paper/1910.05389","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.05389"}},"official":{"repos":["sunlab-osu/MISP"],"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/program-synthesis-and-semantic-parsing-with","slug":"program-synthesis-and-semantic-parsing-with","title":"Program Synthesis and Semantic Parsing with Learned Code Idioms","date":"2019-06-26","arxiv_id":"1906.10816","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/program-synthesis-and-semantic-parsing-with#ran","syntology_url":"https://syntology.ai/paper/1906.10816","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.10816"}},"official":{"repos":["rshin/seq2struct"],"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/sparc-cross-domain-semantic-parsing-in","slug":"sparc-cross-domain-semantic-parsing-in","title":"SParC: Cross-Domain Semantic Parsing in Context","date":"2019-06-05","arxiv_id":"1906.02285","repositories_listed":4,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":3,"n_no_contract":0,"n_pointer_only":3,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 3 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/sparc-cross-domain-semantic-parsing-in#ran","syntology_url":"https://syntology.ai/paper/1906.02285","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.02285"}},"official":{"repos":["ryanzhumich/editsql"],"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","unlocated"]}}},{"url":"/paper/the-neuro-symbolic-concept-learner-1","slug":"the-neuro-symbolic-concept-learner-1","title":"The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision","date":"2019-04-26","arxiv_id":"1904.12584","repositories_listed":2,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/the-neuro-symbolic-concept-learner-1#ran","syntology_url":"https://syntology.ai/paper/1904.12584","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.12584"}},"official":{"repos":["vacancy/NSCL-PyTorch-Release"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/bidirectional-attentive-memory-networks-for","slug":"bidirectional-attentive-memory-networks-for","title":"Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases","date":"2019-03-06","arxiv_id":"1903.02188","repositories_listed":2,"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/bidirectional-attentive-memory-networks-for#ran","syntology_url":"https://syntology.ai/paper/1903.02188","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.02188"}},"official":{"repos":["hugochan/BAMnet"],"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":["listed","official"]}}},{"url":"/paper/a-comprehensive-exploration-on-wikisql-with","slug":"a-comprehensive-exploration-on-wikisql-with","title":"A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization","date":"2019-02-04","arxiv_id":"1902.01069","repositories_listed":5,"syntology":{"n":36,"n_ran":21,"n_constructed":1,"n_ran_checked":5,"n_instrument":16,"n_unverified":15,"n_honours":2,"n_violates":2,"n_no_contract":1,"n_pointer_only":33,"phrase":"21 ran (of which 1 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 2 violated, 1 with no contract checked; 16 where Syntology's instrument failed) · 15 unverified","sample_list":"/paper/a-comprehensive-exploration-on-wikisql-with#ran","syntology_url":"https://syntology.ai/paper/1902.01069","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.01069"}},"official":{"repos":["naver/sqlova"],"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":["listed","official","unlocated"]}}},{"url":"/paper/tranx-a-transition-based-neural-abstract","slug":"tranx-a-transition-based-neural-abstract","title":"TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation","date":"2018-10-05","arxiv_id":"1810.02720","repositories_listed":4,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":2,"phrase":"7 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/tranx-a-transition-based-neural-abstract#ran","syntology_url":"https://syntology.ai/paper/1810.02720","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.02720"}},"official":{"repos":["pcyin/tranX"],"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":["listed","official"]}}},{"url":"/paper/spider-a-large-scale-human-labeled-dataset","slug":"spider-a-large-scale-human-labeled-dataset","title":"Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task","date":"2018-09-24","arxiv_id":"1809.08887","repositories_listed":6,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":1,"n_honours":2,"n_violates":3,"n_no_contract":0,"n_pointer_only":3,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 3 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/spider-a-large-scale-human-labeled-dataset#ran","syntology_url":"https://syntology.ai/paper/1809.08887","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.08887"}},"official":{"repos":["taoyds/spider"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/exploiting-rich-syntactic-information-for","slug":"exploiting-rich-syntactic-information-for","title":"Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model","date":"2018-08-23","arxiv_id":"1808.07624","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/exploiting-rich-syntactic-information-for#ran","syntology_url":"https://syntology.ai/paper/1808.07624","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.07624"}},"official":{"repos":["IBM/Text-to-LogicForm"],"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/robust-text-to-sql-generation-with-execution","slug":"robust-text-to-sql-generation-with-execution","title":"Robust Text-to-SQL Generation with Execution-Guided Decoding","date":"2018-07-09","arxiv_id":"1807.03100","repositories_listed":1,"syntology":{"n":20,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":16,"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) · 16 unverified","sample_list":"/paper/robust-text-to-sql-generation-with-execution#ran","syntology_url":"https://syntology.ai/paper/1807.03100","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.03100"}},"official":null}},{"url":"/paper/memory-augmented-policy-optimization-for","slug":"memory-augmented-policy-optimization-for","title":"Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing","date":"2018-07-06","arxiv_id":"1807.02322","repositories_listed":4,"syntology":{"n":15,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":8,"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) · 8 unverified","sample_list":"/paper/memory-augmented-policy-optimization-for#ran","syntology_url":"https://syntology.ai/paper/1807.02322","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.02322"}},"official":{"repos":["crazydonkey200/neural-symbolic-machines"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/the-natural-language-decathlon-multitask","slug":"the-natural-language-decathlon-multitask","title":"The Natural Language Decathlon: Multitask Learning as Question Answering","date":"2018-06-20","arxiv_id":"1806.08730","repositories_listed":6,"syntology":{"n":13,"n_ran":13,"n_constructed":0,"n_ran_checked":13,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":4,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/the-natural-language-decathlon-multitask#ran","syntology_url":"https://syntology.ai/paper/1806.08730","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.08730"}},"official":{"repos":["salesforce/decaNLP"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/coarse-to-fine-decoding-for-neural-semantic","slug":"coarse-to-fine-decoding-for-neural-semantic","title":"Coarse-to-Fine Decoding for Neural Semantic Parsing","date":"2018-05-12","arxiv_id":"1805.04793","repositories_listed":2,"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/coarse-to-fine-decoding-for-neural-semantic#ran","syntology_url":"https://syntology.ai/paper/1805.04793","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.04793"}},"official":{"repos":["donglixp/coarse2fine"],"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/improving-a-neural-semantic-parser-by","slug":"improving-a-neural-semantic-parser-by","title":"Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit Feedback","date":"2018-05-03","arxiv_id":"1805.01252","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"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 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) · 3 unverified","sample_list":"/paper/improving-a-neural-semantic-parser-by#ran","syntology_url":"https://syntology.ai/paper/1805.01252","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.01252"}},"official":{"repos":["carolinlawrence/nematus"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/nl2bash-a-corpus-and-semantic-parser-for","slug":"nl2bash-a-corpus-and-semantic-parser-for","title":"NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System","date":"2018-02-25","arxiv_id":"1802.08979","repositories_listed":3,"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/nl2bash-a-corpus-and-semantic-parser-for#ran","syntology_url":"https://syntology.ai/paper/1802.08979","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.08979"}},"official":{"repos":["TellinaTool/nl2bash"],"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/weakly-supervised-semantic-parsing-with-1","slug":"weakly-supervised-semantic-parsing-with-1","title":"Weakly-supervised Semantic Parsing with Abstract Examples","date":"2017-11-14","arxiv_id":"1711.05240","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":2,"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/weakly-supervised-semantic-parsing-with-1#ran","syntology_url":"https://syntology.ai/paper/1711.05240","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.05240"}},"official":{"repos":["udiNaveh/nlvr_tau_nlp_final_proj"],"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/sling-a-framework-for-frame-semantic-parsing","slug":"sling-a-framework-for-frame-semantic-parsing","title":"SLING: A framework for frame semantic parsing","date":"2017-10-19","arxiv_id":"1710.07032","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":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"5 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/sling-a-framework-for-frame-semantic-parsing#ran","syntology_url":"https://syntology.ai/paper/1710.07032","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.07032"}},"official":{"repos":["google/sling"],"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/neural-semantic-parsing-by-character-based","slug":"neural-semantic-parsing-by-character-based","title":"Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations","date":"2017-05-28","arxiv_id":"1705.09980","repositories_listed":2,"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":4,"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/neural-semantic-parsing-by-character-based#ran","syntology_url":"https://syntology.ai/paper/1705.09980","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1705.09980"}},"official":{"repos":["RikVN/AMR"],"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/cross-domain-semantic-parsing-via","slug":"cross-domain-semantic-parsing-via","title":"Cross-domain Semantic Parsing via Paraphrasing","date":"2017-04-20","arxiv_id":"1704.05974","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/cross-domain-semantic-parsing-via#ran","syntology_url":"https://syntology.ai/paper/1704.05974","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1704.05974"}},"official":{"repos":["ysu1989/CrossSemparse"],"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/a-syntactic-neural-model-for-general-purpose","slug":"a-syntactic-neural-model-for-general-purpose","title":"A Syntactic Neural Model for General-Purpose Code Generation","date":"2017-04-06","arxiv_id":"1704.01696","repositories_listed":6,"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/a-syntactic-neural-model-for-general-purpose#ran","syntology_url":"https://syntology.ai/paper/1704.01696","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1704.01696"}},"official":null}},{"url":"/paper/pointnet-deep-learning-on-point-sets-for-3d","slug":"pointnet-deep-learning-on-point-sets-for-3d","title":"PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation","date":"2016-12-02","arxiv_id":"1612.00593","repositories_listed":110,"syntology":{"n":164,"n_ran":103,"n_constructed":45,"n_ran_checked":75,"n_instrument":28,"n_unverified":61,"n_honours":0,"n_violates":0,"n_no_contract":75,"n_pointer_only":92,"phrase":"103 ran (of which 45 constructed an object rather than computing a result; 75 with no instrument failure: 0 honoured, 0 violated, 75 with no contract checked; 28 where Syntology's instrument failed) · 61 unverified","sample_list":"/paper/pointnet-deep-learning-on-point-sets-for-3d#ran","syntology_url":"https://syntology.ai/paper/1612.00593","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1612.00593"}},"official":null}},{"url":"/paper/language-to-logical-form-with-neural","slug":"language-to-logical-form-with-neural","title":"Language to Logical Form with Neural Attention","date":"2016-01-06","arxiv_id":"1601.01280","repositories_listed":5,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/language-to-logical-form-with-neural#ran","syntology_url":"https://syntology.ai/paper/1601.01280","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1601.01280"}},"official":{"repos":["donglixp/lang2logic"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}}],"record_sha256":"44d8ba70a4183a77dd050a4c4fc50b53b04c141c9b8e59460b73770a83821257","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}