{"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/articles/papers/ran/2","list_of":"/task/articles","task":"Articles","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":2,"pages_in_order":2,"rows_per_page":100,"rows":[101,126],"of":126,"counts":{"archive_papers_tagged":4012,"with_a_code_link":1123,"where_syntology_ran_a_sample":126,"not_listed_spam_title":0,"listed":4012,"listed_where_code_ran":126,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":107,"every_run_a_failure_of_syntologys_instrument":19,"listed_with_a_run_with_no_instrument_failure":107,"listed_every_run_a_failure_of_syntologys_instrument":19,"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/articles/papers/ran/1","prev":"/task/articles/papers/ran/1","next":null,"papers":[{"url":"/paper/image-based-table-recognition-data-model-and","slug":"image-based-table-recognition-data-model-and","title":"Image-based table recognition: data, model, and evaluation","date":"2019-11-25","arxiv_id":"1911.10683","repositories_listed":6,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/image-based-table-recognition-data-model-and#ran","syntology_url":"https://syntology.ai/paper/1911.10683","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.10683"}},"official":{"repos":["ibm-aur-nlp/PubTabNet"],"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/predicting-the-politics-of-an-image-using","slug":"predicting-the-politics-of-an-image-using","title":"Predicting the Politics of an Image Using Webly Supervised Data","date":"2019-10-31","arxiv_id":"1911.00147","repositories_listed":1,"syntology":{"n":9,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":7,"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) · 7 unverified","sample_list":"/paper/predicting-the-politics-of-an-image-using#ran","syntology_url":"https://syntology.ai/paper/1911.00147","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.00147"}},"official":{"repos":["dragnet-org/dragnet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/mlqa-evaluating-cross-lingual-extractive","slug":"mlqa-evaluating-cross-lingual-extractive","title":"MLQA: Evaluating Cross-lingual Extractive Question Answering","date":"2019-10-16","arxiv_id":"1910.07475","repositories_listed":4,"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/mlqa-evaluating-cross-lingual-extractive#ran","syntology_url":"https://syntology.ai/paper/1910.07475","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.07475"}},"official":{"repos":["facebookresearch/MLQA"],"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/addressing-semantic-drift-in-question","slug":"addressing-semantic-drift-in-question","title":"Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering","date":"2019-09-13","arxiv_id":"1909.06356","repositories_listed":2,"syntology":{"n":19,"n_ran":13,"n_constructed":0,"n_ran_checked":11,"n_instrument":2,"n_unverified":6,"n_honours":2,"n_violates":1,"n_no_contract":8,"n_pointer_only":1,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 2 honoured, 1 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/addressing-semantic-drift-in-question#ran","syntology_url":"https://syntology.ai/paper/1909.06356","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.06356"}},"official":{"repos":["ZhangShiyue/QGforQA"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/question-generation-by-transformers","slug":"question-generation-by-transformers","title":"Question Generation by Transformers","date":"2019-09-09","arxiv_id":"1909.05017","repositories_listed":1,"syntology":{"n":14,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":9,"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) · 9 unverified","sample_list":"/paper/question-generation-by-transformers#ran","syntology_url":"https://syntology.ai/paper/1909.05017","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.05017"}},"official":null}},{"url":"/paper/benchmarking-zero-shot-text-classification","slug":"benchmarking-zero-shot-text-classification","title":"Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach","date":"2019-08-31","arxiv_id":"1909.00161","repositories_listed":4,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/benchmarking-zero-shot-text-classification#ran","syntology_url":"https://syntology.ai/paper/1909.00161","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.00161"}},"official":{"repos":["yinwenpeng/BenchmarkingZeroShot"],"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","unlocated"]}}},{"url":"/paper/earlier-isnt-always-better-sub-aspect","slug":"earlier-isnt-always-better-sub-aspect","title":"Earlier Isn't Always Better: Sub-aspect Analysis on Corpus and System Biases in Summarization","date":"2019-08-30","arxiv_id":"1908.11723","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":3,"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/earlier-isnt-always-better-sub-aspect#ran","syntology_url":"https://syntology.ai/paper/1908.11723","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.11723"}},"official":null}},{"url":"/paper/190807836","slug":"190807836","title":"PubLayNet: largest dataset ever for document layout analysis","date":"2019-08-16","arxiv_id":"1908.07836","repositories_listed":6,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":8,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":13,"phrase":"10 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; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/190807836#ran","syntology_url":"https://syntology.ai/paper/1908.07836","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.07836"}},"official":{"repos":["ibm-aur-nlp/PubLayNet"],"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/the-dynamic-embedded-topic-model","slug":"the-dynamic-embedded-topic-model","title":"The Dynamic Embedded Topic Model","date":"2019-07-12","arxiv_id":"1907.05545","repositories_listed":1,"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/the-dynamic-embedded-topic-model#ran","syntology_url":"https://syntology.ai/paper/1907.05545","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.05545"}},"official":{"repos":["adjidieng/DETM"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/on-the-importance-of-news-content","slug":"on-the-importance-of-news-content","title":"On the Importance of News Content Representation in Hybrid Neural Session-based Recommender Systems","date":"2019-07-12","arxiv_id":"1907.07629","repositories_listed":2,"syntology":{"n":26,"n_ran":16,"n_constructed":0,"n_ran_checked":16,"n_instrument":0,"n_unverified":10,"n_honours":0,"n_violates":0,"n_no_contract":16,"n_pointer_only":0,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 0 honoured, 0 violated, 16 with no contract checked; 0 where Syntology's instrument failed) · 10 unverified","sample_list":"/paper/on-the-importance-of-news-content#ran","syntology_url":"https://syntology.ai/paper/1907.07629","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.07629"}},"official":{"repos":["gabrielspmoreira/chameleon_recsys"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":16,"n_unverified":10,"ran_from_kinds":["official"]}}},{"url":"/paper/wikimatrix-mining-135m-parallel-sentences-in","slug":"wikimatrix-mining-135m-parallel-sentences-in","title":"WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia","date":"2019-07-10","arxiv_id":"1907.05791","repositories_listed":6,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/wikimatrix-mining-135m-parallel-sentences-in#ran","syntology_url":"https://syntology.ai/paper/1907.05791","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.05791"}},"official":{"repos":["facebookresearch/LASER"],"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/neural-arabic-question-answering","slug":"neural-arabic-question-answering","title":"Neural Arabic Question Answering","date":"2019-06-12","arxiv_id":"1906.05394","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/neural-arabic-question-answering#ran","syntology_url":"https://syntology.ai/paper/1906.05394","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.05394"}},"official":{"repos":["husseinmozannar/SOQAL"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/on-the-use-of-arxiv-as-a-dataset","slug":"on-the-use-of-arxiv-as-a-dataset","title":"On the Use of ArXiv as a Dataset","date":"2019-04-30","arxiv_id":"1905.00075","repositories_listed":1,"syntology":{"n":19,"n_ran":15,"n_constructed":0,"n_ran_checked":15,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":0,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/on-the-use-of-arxiv-as-a-dataset#ran","syntology_url":"https://syntology.ai/paper/1905.00075","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.00075"}},"official":{"repos":["mattbierbaum/arxiv-public-datasets"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/end-to-end-open-domain-question-answering","slug":"end-to-end-open-domain-question-answering","title":"End-to-End Open-Domain Question Answering with BERTserini","date":"2019-02-05","arxiv_id":"1902.01718","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":3,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/end-to-end-open-domain-question-answering#ran","syntology_url":"https://syntology.ai/paper/1902.01718","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.01718"}},"official":{"repos":["rsvp-ai/bertserini"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/transformer-xl-attentive-language-models","slug":"transformer-xl-attentive-language-models","title":"Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context","date":"2019-01-09","arxiv_id":"1901.02860","repositories_listed":37,"syntology":{"n":143,"n_ran":67,"n_constructed":37,"n_ran_checked":49,"n_instrument":18,"n_unverified":76,"n_honours":4,"n_violates":1,"n_no_contract":44,"n_pointer_only":43,"phrase":"67 ran (of which 37 constructed an object rather than computing a result; 49 with no instrument failure: 4 honoured, 1 violated, 44 with no contract checked; 18 where Syntology's instrument failed) · 76 unverified","sample_list":"/paper/transformer-xl-attentive-language-models#ran","syntology_url":"https://syntology.ai/paper/1901.02860","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.02860"}},"official":{"repos":["kimiyoung/transformer-xl"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":7,"n_ran_no_instrument_failure":7,"n_unverified":5,"ran_from_kinds":["listed","official","unlocated"]}}},{"url":"/paper/wikihow-a-large-scale-text-summarization","slug":"wikihow-a-large-scale-text-summarization","title":"WikiHow: A Large Scale Text Summarization Dataset","date":"2018-10-18","arxiv_id":"1810.09305","repositories_listed":9,"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/wikihow-a-large-scale-text-summarization#ran","syntology_url":"https://syntology.ai/paper/1810.09305","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.09305"}},"official":null}},{"url":"/paper/indosum-a-new-benchmark-dataset-for","slug":"indosum-a-new-benchmark-dataset-for","title":"IndoSum: A New Benchmark Dataset for Indonesian Text Summarization","date":"2018-10-12","arxiv_id":"1810.05334","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/indosum-a-new-benchmark-dataset-for#ran","syntology_url":"https://syntology.ai/paper/1810.05334","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.05334"}},"official":{"repos":["kata-ai/indosum"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/a-dataset-for-document-grounded-conversations","slug":"a-dataset-for-document-grounded-conversations","title":"A Dataset for Document Grounded Conversations","date":"2018-09-19","arxiv_id":"1809.07358","repositories_listed":3,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-dataset-for-document-grounded-conversations#ran","syntology_url":"https://syntology.ai/paper/1809.07358","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.07358"}},"official":{"repos":["festvox/datasets-CMU_DoG"],"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/dont-give-me-the-details-just-the-summary","slug":"dont-give-me-the-details-just-the-summary","title":"Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization","date":"2018-08-27","arxiv_id":"1808.08745","repositories_listed":3,"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/dont-give-me-the-details-just-the-summary#ran","syntology_url":"https://syntology.ai/paper/1808.08745","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.08745"}},"official":{"repos":["shashiongithub/XSum"],"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/cail2018-a-large-scale-legal-dataset-for","slug":"cail2018-a-large-scale-legal-dataset-for","title":"CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction","date":"2018-07-04","arxiv_id":"1807.02478","repositories_listed":3,"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/cail2018-a-large-scale-legal-dataset-for#ran","syntology_url":"https://syntology.ai/paper/1807.02478","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.02478"}},"official":null}},{"url":"/paper/content-based-citation-recommendation","slug":"content-based-citation-recommendation","title":"Content-Based Citation Recommendation","date":"2018-02-22","arxiv_id":"1802.08301","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/content-based-citation-recommendation#ran","syntology_url":"https://syntology.ai/paper/1802.08301","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.08301"}},"official":{"repos":["allenai/citeomatic"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/neural-models-for-documents-with-metadata","slug":"neural-models-for-documents-with-metadata","title":"Neural Models for Documents with Metadata","date":"2017-05-25","arxiv_id":"1705.09296","repositories_listed":3,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":2,"n_instrument":4,"n_unverified":0,"n_honours":2,"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; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/neural-models-for-documents-with-metadata#ran","syntology_url":"https://syntology.ai/paper/1705.09296","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1705.09296"}},"official":{"repos":["dallascard/neural_topic_models","dallascard/scholar"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/reading-wikipedia-to-answer-open-domain","slug":"reading-wikipedia-to-answer-open-domain","title":"Reading Wikipedia to Answer Open-Domain Questions","date":"2017-03-31","arxiv_id":"1704.00051","repositories_listed":10,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/reading-wikipedia-to-answer-open-domain#ran","syntology_url":"https://syntology.ai/paper/1704.00051","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1704.00051"}},"official":{"repos":["facebookresearch/DrQA"],"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/man-is-to-computer-programmer-as-woman-is-to","slug":"man-is-to-computer-programmer-as-woman-is-to","title":"Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings","date":"2016-07-21","arxiv_id":"1607.06520","repositories_listed":8,"syntology":{"n":8,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/man-is-to-computer-programmer-as-woman-is-to#ran","syntology_url":"https://syntology.ai/paper/1607.06520","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1607.06520"}},"official":null}},{"url":"/paper/squad-100000-questions-for-machine","slug":"squad-100000-questions-for-machine","title":"SQuAD: 100,000+ Questions for Machine Comprehension of Text","date":"2016-06-16","arxiv_id":"1606.05250","repositories_listed":21,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":6,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/squad-100000-questions-for-machine#ran","syntology_url":"https://syntology.ai/paper/1606.05250","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1606.05250"}},"official":{"repos":["worksheets.codalab.org/worksheets/0xd53d03a48ef64b329c16b9baf0f99b0c"],"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-contextual-bandit-approach-to-personalized","slug":"a-contextual-bandit-approach-to-personalized","title":"A Contextual-Bandit Approach to Personalized News Article Recommendation","date":"2010-02-28","arxiv_id":"1003.0146","repositories_listed":12,"syntology":{"n":3,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"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) · 2 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/a-contextual-bandit-approach-to-personalized#ran","syntology_url":"https://syntology.ai/paper/1003.0146","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1003.0146"}},"official":null}}],"record_sha256":"a4ab595c1e4f17dd2ec4bcbac06c65d7027c540967b1866498af087843422c45","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}