{"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/word-embeddings/papers/ran/2","list_of":"/task/word-embeddings","task":"Word Embeddings","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":[101,155],"of":155,"counts":{"archive_papers_tagged":4002,"with_a_code_link":1177,"where_syntology_ran_a_sample":155,"not_listed_spam_title":0,"listed":4002,"listed_where_code_ran":155,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":122,"every_run_a_failure_of_syntologys_instrument":33,"listed_with_a_run_with_no_instrument_failure":122,"listed_every_run_a_failure_of_syntologys_instrument":33,"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/word-embeddings/papers/ran/1","prev":"/task/word-embeddings/papers/ran/1","next":null,"papers":[{"url":"/paper/structured-pruning-of-large-language-models","slug":"structured-pruning-of-large-language-models","title":"Structured Pruning of Large Language Models","date":"2019-10-10","arxiv_id":"1910.04732","repositories_listed":2,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/structured-pruning-of-large-language-models#ran","syntology_url":"https://syntology.ai/paper/1910.04732","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.04732"}},"official":{"repos":["asappresearch/flop"],"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-general-framework-for-implicit-and-explicit","slug":"a-general-framework-for-implicit-and-explicit","title":"A General Framework for Implicit and Explicit Debiasing of Distributional Word Vector Spaces","date":"2019-09-13","arxiv_id":"1909.06092","repositories_listed":4,"syntology":{"n":19,"n_ran":18,"n_constructed":0,"n_ran_checked":18,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/a-general-framework-for-implicit-and-explicit#ran","syntology_url":"https://syntology.ai/paper/1909.06092","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.06092"}},"official":null}},{"url":"/paper/semantics-aware-bert-for-language","slug":"semantics-aware-bert-for-language","title":"Semantics-aware BERT for Language Understanding","date":"2019-09-05","arxiv_id":"1909.02209","repositories_listed":1,"syntology":{"n":12,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":5,"n_honours":2,"n_violates":0,"n_no_contract":3,"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, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/semantics-aware-bert-for-language#ran","syntology_url":"https://syntology.ai/paper/1909.02209","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.02209"}},"official":{"repos":["cooelf/SemBERT"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/on-the-downstream-performance-of-compressed","slug":"on-the-downstream-performance-of-compressed","title":"On the Downstream Performance of Compressed Word Embeddings","date":"2019-09-03","arxiv_id":"1909.01264","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":3,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/on-the-downstream-performance-of-compressed#ran","syntology_url":"https://syntology.ai/paper/1909.01264","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.01264"}},"official":{"repos":["HazyResearch/smallfry"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/multimodal-deep-networks-for-text-and-image","slug":"multimodal-deep-networks-for-text-and-image","title":"Multimodal deep networks for text and image-based document classification","date":"2019-07-15","arxiv_id":"1907.06370","repositories_listed":3,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/multimodal-deep-networks-for-text-and-image#ran","syntology_url":"https://syntology.ai/paper/1907.06370","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.06370"}},"official":{"repos":["Quicksign/ocrized-text-dataset"],"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/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/topic-modeling-in-embedding-spaces","slug":"topic-modeling-in-embedding-spaces","title":"Topic Modeling in Embedding Spaces","date":"2019-07-08","arxiv_id":"1907.04907","repositories_listed":12,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":2,"n_no_contract":7,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 2 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/topic-modeling-in-embedding-spaces#ran","syntology_url":"https://syntology.ai/paper/1907.04907","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.04907"}},"official":{"repos":["adjidieng/ETM"],"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"]}}},{"url":"/paper/few-shot-representation-learning-for-out-of","slug":"few-shot-representation-learning-for-out-of","title":"Few-Shot Representation Learning for Out-Of-Vocabulary Words","date":"2019-07-01","arxiv_id":"1907.00505","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/few-shot-representation-learning-for-out-of#ran","syntology_url":"https://syntology.ai/paper/1907.00505","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.00505"}},"official":{"repos":["acbull/HiCE"],"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/variational-sequential-labelers-for-semi-1","slug":"variational-sequential-labelers-for-semi-1","title":"Variational Sequential Labelers for Semi-Supervised Learning","date":"2019-06-23","arxiv_id":"1906.09535","repositories_listed":1,"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/variational-sequential-labelers-for-semi-1#ran","syntology_url":"https://syntology.ai/paper/1906.09535","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.09535"}},"official":{"repos":["mingdachen/vsl"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/tracing-antisemitic-language-through","slug":"tracing-antisemitic-language-through","title":"Tracing Antisemitic Language Through Diachronic Embedding Projections: France 1789-1914","date":"2019-06-04","arxiv_id":"1906.01440","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/tracing-antisemitic-language-through#ran","syntology_url":"https://syntology.ai/paper/1906.01440","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.01440"}},"official":{"repos":["roccotrip/antisem"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/sherlock-a-deep-learning-approach-to-semantic","slug":"sherlock-a-deep-learning-approach-to-semantic","title":"Sherlock: A Deep Learning Approach to Semantic Data Type Detection","date":"2019-05-25","arxiv_id":"1905.10688","repositories_listed":2,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/sherlock-a-deep-learning-approach-to-semantic#ran","syntology_url":"https://syntology.ai/paper/1905.10688","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.10688"}},"official":{"repos":["mitmedialab/sherlock-project"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/augmenting-data-with-mixup-for-sentence","slug":"augmenting-data-with-mixup-for-sentence","title":"Augmenting Data with Mixup for Sentence Classification: An Empirical Study","date":"2019-05-22","arxiv_id":"1905.08941","repositories_listed":3,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/augmenting-data-with-mixup-for-sentence#ran","syntology_url":"https://syntology.ai/paper/1905.08941","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.08941"}},"official":null}},{"url":"/paper/deeper-text-understanding-for-ir-with","slug":"deeper-text-understanding-for-ir-with","title":"Deeper Text Understanding for IR with Contextual Neural Language Modeling","date":"2019-05-22","arxiv_id":"1905.09217","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"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) · 4 unverified","sample_list":"/paper/deeper-text-understanding-for-ir-with#ran","syntology_url":"https://syntology.ai/paper/1905.09217","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.09217"}},"official":{"repos":["AdeDZY/SIGIR19-BERT-IR"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/analytical-methods-for-interpretable","slug":"analytical-methods-for-interpretable","title":"Analytical Methods for Interpretable Ultradense Word Embeddings","date":"2019-04-18","arxiv_id":"1904.08654","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"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: 0 honoured, 1 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/analytical-methods-for-interpretable#ran","syntology_url":"https://syntology.ai/paper/1904.08654","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.08654"}},"official":{"repos":["pdufter/densray"],"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":["official"]}}},{"url":"/paper/gender-bias-in-contextualized-word-embeddings","slug":"gender-bias-in-contextualized-word-embeddings","title":"Gender Bias in Contextualized Word Embeddings","date":"2019-04-05","arxiv_id":"1904.03310","repositories_listed":2,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/gender-bias-in-contextualized-word-embeddings#ran","syntology_url":"https://syntology.ai/paper/1904.03310","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.03310"}},"official":null}},{"url":"/paper/density-matching-for-bilingual-word-embedding","slug":"density-matching-for-bilingual-word-embedding","title":"Density Matching for Bilingual Word Embedding","date":"2019-04-04","arxiv_id":"1904.02343","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/density-matching-for-bilingual-word-embedding#ran","syntology_url":"https://syntology.ai/paper/1904.02343","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.02343"}},"official":{"repos":["violet-zct/DeMa-BWE"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/black-is-to-criminal-as-caucasian-is-to","slug":"black-is-to-criminal-as-caucasian-is-to","title":"Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings","date":"2019-04-03","arxiv_id":"1904.04047","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/black-is-to-criminal-as-caucasian-is-to#ran","syntology_url":"https://syntology.ai/paper/1904.04047","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.04047"}},"official":{"repos":["TManzini/DebiasMulticlassWordEmbedding"],"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/attentive-mimicking-better-word-embeddings-by","slug":"attentive-mimicking-better-word-embeddings-by","title":"Attentive Mimicking: Better Word Embeddings by Attending to Informative Contexts","date":"2019-04-02","arxiv_id":"1904.01617","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/attentive-mimicking-better-word-embeddings-by#ran","syntology_url":"https://syntology.ai/paper/1904.01617","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.01617"}},"official":{"repos":["timoschick/form-context-model"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/integrating-semantic-knowledge-to-tackle-zero","slug":"integrating-semantic-knowledge-to-tackle-zero","title":"Integrating Semantic Knowledge to Tackle Zero-shot Text Classification","date":"2019-03-29","arxiv_id":"1903.12626","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/integrating-semantic-knowledge-to-tackle-zero#ran","syntology_url":"https://syntology.ai/paper/1903.12626","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.12626"}},"official":{"repos":["JingqingZ/KG4ZeroShotText"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/lipstick-on-a-pig-debiasing-methods-cover-up","slug":"lipstick-on-a-pig-debiasing-methods-cover-up","title":"Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them","date":"2019-03-09","arxiv_id":"1903.03862","repositories_listed":2,"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/lipstick-on-a-pig-debiasing-methods-cover-up#ran","syntology_url":"https://syntology.ai/paper/1903.03862","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.03862"}},"official":{"repos":["gonenhila/gender_bias_lipstick"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/cross-lingual-alignment-of-contextual-word","slug":"cross-lingual-alignment-of-contextual-word","title":"Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing","date":"2019-02-25","arxiv_id":"1902.09492","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/cross-lingual-alignment-of-contextual-word#ran","syntology_url":"https://syntology.ai/paper/1902.09492","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.09492"}},"official":{"repos":["TalSchuster/CrossLingualELMo"],"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":["listed","official"]}}},{"url":"/paper/wasserstein-barycenter-model-ensembling-1","slug":"wasserstein-barycenter-model-ensembling-1","title":"Wasserstein Barycenter Model Ensembling","date":"2019-02-13","arxiv_id":"1902.04999","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/wasserstein-barycenter-model-ensembling-1#ran","syntology_url":"https://syntology.ai/paper/1902.04999","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.04999"}},"official":null}},{"url":"/paper/understanding-composition-of-word-embeddings","slug":"understanding-composition-of-word-embeddings","title":"Understanding Composition of Word Embeddings via Tensor Decomposition","date":"2019-02-02","arxiv_id":"1902.00613","repositories_listed":2,"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/understanding-composition-of-word-embeddings#ran","syntology_url":"https://syntology.ai/paper/1902.00613","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.00613"}},"official":{"repos":["abefrandsen/syntactic-rand-walk"],"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-dimensionality-of-word-embedding","slug":"on-the-dimensionality-of-word-embedding","title":"On the Dimensionality of Word Embedding","date":"2018-12-11","arxiv_id":"1812.04224","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/on-the-dimensionality-of-word-embedding#ran","syntology_url":"https://syntology.ai/paper/1812.04224","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.04224"}},"official":{"repos":["ziyin-dl/word-embedding-dimensionality-selection","aaaasssddf/PIP-experiments"],"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/texttovec-deep-contextualized-neural","slug":"texttovec-deep-contextualized-neural","title":"textTOvec: Deep Contextualized Neural Autoregressive Topic Models of Language with Distributed Compositional Prior","date":"2018-10-09","arxiv_id":"1810.03947","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/texttovec-deep-contextualized-neural#ran","syntology_url":"https://syntology.ai/paper/1810.03947","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.03947"}},"official":{"repos":["pgcool/textTOvec"],"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/unsupervised-cross-lingual-transfer-of-word","slug":"unsupervised-cross-lingual-transfer-of-word","title":"Unsupervised Cross-lingual Transfer of Word Embedding Spaces","date":"2018-09-10","arxiv_id":"1809.03633","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/unsupervised-cross-lingual-transfer-of-word#ran","syntology_url":"https://syntology.ai/paper/1809.03633","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.03633"}},"official":{"repos":["xrc10/unsup-cross-lingual-embedding-transfer"],"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/analogical-reasoning-on-chinese-morphological","slug":"analogical-reasoning-on-chinese-morphological","title":"Analogical Reasoning on Chinese Morphological and Semantic Relations","date":"2018-05-12","arxiv_id":"1805.06504","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/analogical-reasoning-on-chinese-morphological#ran","syntology_url":"https://syntology.ai/paper/1805.06504","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.06504"}},"official":{"repos":["Embedding/Chinese-Word-Vectors"],"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/factors-influencing-the-surprising","slug":"factors-influencing-the-surprising","title":"Factors Influencing the Surprising Instability of Word Embeddings","date":"2018-04-25","arxiv_id":"1804.09692","repositories_listed":2,"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/factors-influencing-the-surprising#ran","syntology_url":"https://syntology.ai/paper/1804.09692","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.09692"}},"official":{"repos":["laura-burdick/embeddingStability"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/linguistically-informed-self-attention-for","slug":"linguistically-informed-self-attention-for","title":"Linguistically-Informed Self-Attention for Semantic Role Labeling","date":"2018-04-23","arxiv_id":"1804.08199","repositories_listed":1,"syntology":{"n":14,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":5,"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) · 5 unverified","sample_list":"/paper/linguistically-informed-self-attention-for#ran","syntology_url":"https://syntology.ai/paper/1804.08199","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.08199"}},"official":{"repos":["strubell/LISA"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/universal-sentence-encoder","slug":"universal-sentence-encoder","title":"Universal Sentence Encoder","date":"2018-03-29","arxiv_id":"1803.11175","repositories_listed":24,"syntology":{"n":22,"n_ran":18,"n_constructed":0,"n_ran_checked":17,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":17,"n_pointer_only":1,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 0 violated, 17 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/universal-sentence-encoder#ran","syntology_url":"https://syntology.ai/paper/1803.11175","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.11175"}},"official":null}},{"url":"/paper/concatenated-power-mean-word-embeddings-as","slug":"concatenated-power-mean-word-embeddings-as","title":"Concatenated Power Mean Word Embeddings as Universal Cross-Lingual Sentence Representations","date":"2018-03-04","arxiv_id":"1803.01400","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/concatenated-power-mean-word-embeddings-as#ran","syntology_url":"https://syntology.ai/paper/1803.01400","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.01400"}},"official":{"repos":["UKPLab/arxiv2018-xling-sentence-embeddings"],"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/spine-sparse-interpretable-neural-embeddings","slug":"spine-sparse-interpretable-neural-embeddings","title":"SPINE: SParse Interpretable Neural Embeddings","date":"2017-11-23","arxiv_id":"1711.08792","repositories_listed":2,"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/spine-sparse-interpretable-neural-embeddings#ran","syntology_url":"https://syntology.ai/paper/1711.08792","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.08792"}},"official":{"repos":["harsh19/SPINE"],"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/breaking-the-softmax-bottleneck-a-high-rank","slug":"breaking-the-softmax-bottleneck-a-high-rank","title":"Breaking the Softmax Bottleneck: A High-Rank RNN Language Model","date":"2017-11-10","arxiv_id":"1711.03953","repositories_listed":9,"syntology":{"n":23,"n_ran":12,"n_constructed":0,"n_ran_checked":11,"n_instrument":1,"n_unverified":11,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":1,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 1 where Syntology's instrument failed) · 11 unverified","sample_list":"/paper/breaking-the-softmax-bottleneck-a-high-rank#ran","syntology_url":"https://syntology.ai/paper/1711.03953","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.03953"}},"official":{"repos":["zihangdai/mos"],"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":["listed","official"]}}},{"url":"/paper/word-translation-without-parallel-data","slug":"word-translation-without-parallel-data","title":"Word Translation Without Parallel Data","date":"2017-10-11","arxiv_id":"1710.04087","repositories_listed":20,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":2,"n_instrument":4,"n_unverified":2,"n_honours":0,"n_violates":2,"n_no_contract":0,"n_pointer_only":8,"phrase":"6 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; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/word-translation-without-parallel-data#ran","syntology_url":"https://syntology.ai/paper/1710.04087","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.04087"}},"official":{"repos":["facebookresearch/MUSE"],"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/improving-lexical-choice-in-neural-machine","slug":"improving-lexical-choice-in-neural-machine","title":"Improving Lexical Choice in Neural Machine Translation","date":"2017-10-03","arxiv_id":"1710.01329","repositories_listed":4,"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/improving-lexical-choice-in-neural-machine#ran","syntology_url":"https://syntology.ai/paper/1710.01329","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.01329"}},"official":{"repos":["tnq177/improving_lexical_choice_in_nmt"],"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/mimicking-word-embeddings-using-subword-rnns","slug":"mimicking-word-embeddings-using-subword-rnns","title":"Mimicking Word Embeddings using Subword RNNs","date":"2017-07-21","arxiv_id":"1707.06961","repositories_listed":2,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"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) · 2 unverified","sample_list":"/paper/mimicking-word-embeddings-using-subword-rnns#ran","syntology_url":"https://syntology.ai/paper/1707.06961","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1707.06961"}},"official":{"repos":["yuvalpinter/Mimick"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/supervised-learning-of-universal-sentence","slug":"supervised-learning-of-universal-sentence","title":"Supervised Learning of Universal Sentence Representations from Natural Language Inference Data","date":"2017-05-05","arxiv_id":"1705.02364","repositories_listed":23,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":2,"n_instrument":4,"n_unverified":1,"n_honours":2,"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; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/supervised-learning-of-universal-sentence#ran","syntology_url":"https://syntology.ai/paper/1705.02364","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1705.02364"}},"official":{"repos":["facebookresearch/InferSent","facebookresearch/SentEval"],"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":["listed","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/an-embedded-segmental-k-means-model-for","slug":"an-embedded-segmental-k-means-model-for","title":"An embedded segmental K-means model for unsupervised segmentation and clustering of speech","date":"2017-03-23","arxiv_id":"1703.08135","repositories_listed":2,"syntology":{"n":15,"n_ran":13,"n_constructed":0,"n_ran_checked":10,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":3,"phrase":"13 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; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/an-embedded-segmental-k-means-model-for#ran","syntology_url":"https://syntology.ai/paper/1703.08135","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.08135"}},"official":null}},{"url":"/paper/dynamic-word-embeddings","slug":"dynamic-word-embeddings","title":"Dynamic Word Embeddings","date":"2017-02-27","arxiv_id":"1702.08359","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dynamic-word-embeddings#ran","syntology_url":"https://syntology.ai/paper/1702.08359","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1702.08359"}},"official":null}},{"url":"/paper/fasttextzip-compressing-text-classification","slug":"fasttextzip-compressing-text-classification","title":"FastText.zip: Compressing text classification models","date":"2016-12-12","arxiv_id":"1612.03651","repositories_listed":44,"syntology":{"n":18,"n_ran":11,"n_constructed":0,"n_ran_checked":8,"n_instrument":3,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":13,"phrase":"11 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; 3 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/fasttextzip-compressing-text-classification#ran","syntology_url":"https://syntology.ai/paper/1612.03651","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1612.03651"}},"official":{"repos":["facebookresearch/fastText"],"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/conceptnet-55-an-open-multilingual-graph-of","slug":"conceptnet-55-an-open-multilingual-graph-of","title":"ConceptNet 5.5: An Open Multilingual Graph of General Knowledge","date":"2016-12-12","arxiv_id":"1612.03975","repositories_listed":6,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/conceptnet-55-an-open-multilingual-graph-of#ran","syntology_url":"https://syntology.ai/paper/1612.03975","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1612.03975"}},"official":{"repos":["commonsense/conceptnet-numberbatch"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/definition-modeling-learning-to-define-word","slug":"definition-modeling-learning-to-define-word","title":"Definition Modeling: Learning to define word embeddings in natural language","date":"2016-12-01","arxiv_id":"1612.00394","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/definition-modeling-learning-to-define-word#ran","syntology_url":"https://syntology.ai/paper/1612.00394","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1612.00394"}},"official":{"repos":["northanapon/dict-definition","websail-nu/torch-defseq"],"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/compressing-neural-language-models-by-sparse","slug":"compressing-neural-language-models-by-sparse","title":"Compressing Neural Language Models by Sparse Word Representations","date":"2016-10-13","arxiv_id":"1610.03950","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/compressing-neural-language-models-by-sparse#ran","syntology_url":"https://syntology.ai/paper/1610.03950","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1610.03950"}},"official":{"repos":["chenych11/lm"],"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/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/enriching-word-vectors-with-subword","slug":"enriching-word-vectors-with-subword","title":"Enriching Word Vectors with Subword Information","date":"2016-07-15","arxiv_id":"1607.04606","repositories_listed":54,"syntology":{"n":31,"n_ran":25,"n_constructed":0,"n_ran_checked":21,"n_instrument":4,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":21,"n_pointer_only":4,"phrase":"25 ran (of which 0 constructed an object rather than computing a result; 21 with no instrument failure: 0 honoured, 0 violated, 21 with no contract checked; 4 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/enriching-word-vectors-with-subword#ran","syntology_url":"https://syntology.ai/paper/1607.04606","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1607.04606"}},"official":{"repos":["facebookresearch/fastText"],"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/inducing-domain-specific-sentiment-lexicons","slug":"inducing-domain-specific-sentiment-lexicons","title":"Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora","date":"2016-06-09","arxiv_id":"1606.02820","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/inducing-domain-specific-sentiment-lexicons#ran","syntology_url":"https://syntology.ai/paper/1606.02820","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1606.02820"}},"official":null}},{"url":"/paper/diachronic-word-embeddings-reveal-statistical","slug":"diachronic-word-embeddings-reveal-statistical","title":"Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change","date":"2016-05-30","arxiv_id":"1605.09096","repositories_listed":6,"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/diachronic-word-embeddings-reveal-statistical#ran","syntology_url":"https://syntology.ai/paper/1605.09096","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1605.09096"}},"official":null}},{"url":"/paper/mixing-dirichlet-topic-models-and-word","slug":"mixing-dirichlet-topic-models-and-word","title":"Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec","date":"2016-05-06","arxiv_id":"1605.02019","repositories_listed":5,"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/mixing-dirichlet-topic-models-and-word#ran","syntology_url":"https://syntology.ai/paper/1605.02019","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1605.02019"}},"official":{"repos":["cemoody/lda2vec"],"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/nonparametric-spherical-topic-modeling-with","slug":"nonparametric-spherical-topic-modeling-with","title":"Nonparametric Spherical Topic Modeling with Word Embeddings","date":"2016-04-01","arxiv_id":"1604.00126","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/nonparametric-spherical-topic-modeling-with#ran","syntology_url":"https://syntology.ai/paper/1604.00126","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1604.00126"}},"official":null}},{"url":"/paper/named-entity-recognition-with-bidirectional","slug":"named-entity-recognition-with-bidirectional","title":"Named Entity Recognition with Bidirectional LSTM-CNNs","date":"2015-11-26","arxiv_id":"1511.08308","repositories_listed":14,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":5,"n_pointer_only":4,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 2 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/named-entity-recognition-with-bidirectional#ran","syntology_url":"https://syntology.ai/paper/1511.08308","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1511.08308"}},"official":null}},{"url":"/paper/building-end-to-end-dialogue-systems-using","slug":"building-end-to-end-dialogue-systems-using","title":"Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models","date":"2015-07-17","arxiv_id":"1507.04808","repositories_listed":7,"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/building-end-to-end-dialogue-systems-using#ran","syntology_url":"https://syntology.ai/paper/1507.04808","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1507.04808"}},"official":null}},{"url":"/paper/learning-language-through-pictures","slug":"learning-language-through-pictures","title":"Learning language through pictures","date":"2015-06-11","arxiv_id":"1506.03694","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-language-through-pictures#ran","syntology_url":"https://syntology.ai/paper/1506.03694","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1506.03694"}},"official":{"repos":["gchrupala/imaginet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/classifying-relations-by-ranking-with","slug":"classifying-relations-by-ranking-with","title":"Classifying Relations by Ranking with Convolutional Neural Networks","date":"2015-04-24","arxiv_id":"1504.06580","repositories_listed":2,"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/classifying-relations-by-ranking-with#ran","syntology_url":"https://syntology.ai/paper/1504.06580","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1504.06580"}},"official":null}},{"url":"/paper/word2vec-explained-deriving-mikolov-et-als","slug":"word2vec-explained-deriving-mikolov-et-als","title":"word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method","date":"2014-02-15","arxiv_id":"1402.3722","repositories_listed":5,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/word2vec-explained-deriving-mikolov-et-als#ran","syntology_url":"https://syntology.ai/paper/1402.3722","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1402.3722"}},"official":null}}],"record_sha256":"e89362525c9ec79f1a872a5055f2e9a61a11841950422cdcd7ec70a7d05c483b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}