{"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/4","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":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":4,"pages_in_order":41,"rows_per_page":100,"rows":[301,400],"of":4002,"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","prev":"/task/word-embeddings/papers/3","next":"/task/word-embeddings/papers/5","papers":[{"url":"/paper/multi-relational-hyperbolic-word-embeddings","slug":"multi-relational-hyperbolic-word-embeddings","title":"Multi-Relational Hyperbolic Word Embeddings from Natural Language Definitions","date":"2023-05-12","arxiv_id":"2305.07303","repositories_listed":1,"syntology":null},{"url":"/paper/boosting-zero-shot-cross-lingual-retrieval-by","slug":"boosting-zero-shot-cross-lingual-retrieval-by","title":"Boosting Zero-shot Cross-lingual Retrieval by Training on Artificially Code-Switched Data","date":"2023-05-09","arxiv_id":"2305.05295","repositories_listed":1,"syntology":null},{"url":"/paper/are-the-best-multilingual-document-embeddings","slug":"are-the-best-multilingual-document-embeddings","title":"Are the Best Multilingual Document Embeddings simply Based on Sentence Embeddings?","date":"2023-04-28","arxiv_id":"2304.14796","repositories_listed":1,"syntology":null},{"url":"/paper/analyzing-vietnamese-legal-questions-using-1","slug":"analyzing-vietnamese-legal-questions-using-1","title":"Analyzing Vietnamese Legal Questions Using Deep Neural Networks with Biaffine Classifiers","date":"2023-04-27","arxiv_id":"2304.14447","repositories_listed":1,"syntology":null},{"url":"/paper/simplex-a-lexical-text-simplification","slug":"simplex-a-lexical-text-simplification","title":"SimpLex: a lexical text simplification architecture","date":"2023-04-14","arxiv_id":"2304.07002","repositories_listed":1,"syntology":null},{"url":"/paper/towards-preserving-word-order-importance","slug":"towards-preserving-word-order-importance","title":"Towards preserving word order importance through Forced Invalidation","date":"2023-04-11","arxiv_id":"2304.05221","repositories_listed":1,"syntology":null},{"url":"/paper/pwesuite-phonetic-word-embeddings-and-tasks","slug":"pwesuite-phonetic-word-embeddings-and-tasks","title":"PWESuite: Phonetic Word Embeddings and Tasks They Facilitate","date":"2023-04-05","arxiv_id":"2304.02541","repositories_listed":1,"syntology":null},{"url":"/paper/ctran-cnn-transformer-based-network-for","slug":"ctran-cnn-transformer-based-network-for","title":"CTRAN: CNN-Transformer-based Network for Natural Language Understanding","date":"2023-03-19","arxiv_id":"2303.10606","repositories_listed":1,"syntology":null},{"url":"/paper/neural-gromov-wasserstein-optimal-transport","slug":"neural-gromov-wasserstein-optimal-transport","title":"Uncovering Challenges of Solving the Continuous Gromov-Wasserstein Problem","date":"2023-03-10","arxiv_id":"2303.05978","repositories_listed":1,"syntology":null},{"url":"/paper/landmark-language-guided-representation","slug":"landmark-language-guided-representation","title":"LANDMARK: Language-guided Representation Enhancement Framework for Scene Graph Generation","date":"2023-03-02","arxiv_id":"2303.01080","repositories_listed":1,"syntology":null},{"url":"/paper/sanskritshala-a-neural-sanskrit-nlp-toolkit","slug":"sanskritshala-a-neural-sanskrit-nlp-toolkit","title":"SanskritShala: A Neural Sanskrit NLP Toolkit with Web-Based Interface for Pedagogical and Annotation Purposes","date":"2023-02-19","arxiv_id":"2302.09527","repositories_listed":1,"syntology":null},{"url":"/paper/retvec-resilient-and-efficient-text","slug":"retvec-resilient-and-efficient-text","title":"RETVec: Resilient and Efficient Text Vectorizer","date":"2023-02-18","arxiv_id":"2302.09207","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":2,"n_ran_checked":2,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/retvec-resilient-and-efficient-text#ran","syntology_url":"https://syntology.ai/paper/2302.09207","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.09207"}},"official":{"repos":["google-research/retvec"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/zero-shot-learning-for-requirements","slug":"zero-shot-learning-for-requirements","title":"Zero-Shot Learning for Requirements Classification: An Exploratory Study","date":"2023-02-09","arxiv_id":"2302.04723","repositories_listed":1,"syntology":null},{"url":"/paper/language-embeddings-sometimes-contain","slug":"language-embeddings-sometimes-contain","title":"Language Embeddings Sometimes Contain Typological Generalizations","date":"2023-01-19","arxiv_id":"2301.08115","repositories_listed":1,"syntology":null},{"url":"/paper/the-2022-n2c2-uw-shared-task-on-extracting","slug":"the-2022-n2c2-uw-shared-task-on-extracting","title":"The 2022 n2c2/UW Shared Task on Extracting Social Determinants of Health","date":"2023-01-13","arxiv_id":"2301.05571","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":10,"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/the-2022-n2c2-uw-shared-task-on-extracting#ran","syntology_url":"https://syntology.ai/paper/2301.05571","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.05571"}},"official":null}},{"url":"/paper/sensepolar-word-sense-aware-interpretability","slug":"sensepolar-word-sense-aware-interpretability","title":"SensePOLAR: Word sense aware interpretability for pre-trained contextual word embeddings","date":"2023-01-11","arxiv_id":"2301.04704","repositories_listed":1,"syntology":null},{"url":"/paper/supervised-acoustic-embeddings-and-their","slug":"supervised-acoustic-embeddings-and-their","title":"Supervised Acoustic Embeddings And Their Transferability Across Languages","date":"2023-01-03","arxiv_id":"2301.01020","repositories_listed":1,"syntology":null},{"url":"/paper/the-undesirable-dependence-on-frequency-of","slug":"the-undesirable-dependence-on-frequency-of","title":"The Undesirable Dependence on Frequency of Gender Bias Metrics Based on Word Embeddings","date":"2023-01-02","arxiv_id":"2301.00792","repositories_listed":1,"syntology":null},{"url":"/paper/do-conll-2003-named-entity-taggers-still-work","slug":"do-conll-2003-named-entity-taggers-still-work","title":"Do CoNLL-2003 Named Entity Taggers Still Work Well in 2023?","date":"2022-12-19","arxiv_id":"2212.09747","repositories_listed":1,"syntology":null},{"url":"/paper/multi-hash-embeddings-in-spacy","slug":"multi-hash-embeddings-in-spacy","title":"Multi hash embeddings in spaCy","date":"2022-12-19","arxiv_id":"2212.09255","repositories_listed":1,"syntology":null},{"url":"/paper/effective-seed-guided-topic-discovery-by","slug":"effective-seed-guided-topic-discovery-by","title":"Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contexts","date":"2022-12-12","arxiv_id":"2212.06002","repositories_listed":1,"syntology":null},{"url":"/paper/rpn-a-word-vector-level-data-augmentation","slug":"rpn-a-word-vector-level-data-augmentation","title":"RPN: A Word Vector Level Data Augmentation Algorithm in Deep Learning for Language Understanding","date":"2022-12-12","arxiv_id":"2212.05961","repositories_listed":1,"syntology":null},{"url":"/paper/a-study-of-slang-representation-methods","slug":"a-study-of-slang-representation-methods","title":"A Study of Slang Representation Methods","date":"2022-12-11","arxiv_id":"2212.05613","repositories_listed":1,"syntology":null},{"url":"/paper/tracking-semantic-shifts-in-german-court","slug":"tracking-semantic-shifts-in-german-court","title":"Tracking Semantic Shifts in German Court Decisions with Diachronic Word Embeddings","date":"2022-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-object-language-alignments-for-open","slug":"learning-object-language-alignments-for-open","title":"Learning Object-Language Alignments for Open-Vocabulary Object Detection","date":"2022-11-27","arxiv_id":"2211.14843","repositories_listed":1,"syntology":null},{"url":"/paper/align-mlm-word-embedding-alignment-is-crucial","slug":"align-mlm-word-embedding-alignment-is-crucial","title":"ALIGN-MLM: Word Embedding Alignment is Crucial for Multilingual Pre-training","date":"2022-11-15","arxiv_id":"2211.08547","repositories_listed":1,"syntology":null},{"url":"/paper/mind-your-bias-a-critical-review-of-bias","slug":"mind-your-bias-a-critical-review-of-bias","title":"Mind Your Bias: A Critical Review of Bias Detection Methods for Contextual Language Models","date":"2022-11-15","arxiv_id":"2211.08461","repositories_listed":1,"syntology":null},{"url":"/paper/sexwes-domain-aware-word-embeddings-via-cross","slug":"sexwes-domain-aware-word-embeddings-via-cross","title":"SexWEs: Domain-Aware Word Embeddings via Cross-lingual Semantic Specialisation for Chinese Sexism Detection in Social Media","date":"2022-11-15","arxiv_id":"2211.08447","repositories_listed":1,"syntology":null},{"url":"/paper/the-dependence-on-frequency-of-word-embedding","slug":"the-dependence-on-frequency-of-word-embedding","title":"Investigating the Frequency Distortion of Word Embeddings and Its Impact on Bias Metrics","date":"2022-11-15","arxiv_id":"2211.08203","repositories_listed":1,"syntology":null},{"url":"/paper/improving-word-mover-s-distance-by-leveraging","slug":"improving-word-mover-s-distance-by-leveraging","title":"Improving word mover's distance by leveraging self-attention matrix","date":"2022-11-11","arxiv_id":"2211.06229","repositories_listed":1,"syntology":null},{"url":"/paper/adept-a-debiasing-prompt-framework","slug":"adept-a-debiasing-prompt-framework","title":"ADEPT: A DEbiasing PrompT Framework","date":"2022-11-10","arxiv_id":"2211.05414","repositories_listed":1,"syntology":{"n":13,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":5,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/adept-a-debiasing-prompt-framework#ran","syntology_url":"https://syntology.ai/paper/2211.05414","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.05414"}},"official":{"repos":["EmpathYang/ADEPT"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/improving-bilingual-lexicon-induction-with-1","slug":"improving-bilingual-lexicon-induction-with-1","title":"Improving Bilingual Lexicon Induction with Cross-Encoder Reranking","date":"2022-10-30","arxiv_id":"2210.16953","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/improving-bilingual-lexicon-induction-with-1#ran","syntology_url":"https://syntology.ai/paper/2210.16953","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.16953"}},"official":{"repos":["cambridgeltl/BLICEr"],"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/using-context-to-vector-with-graph-1","slug":"using-context-to-vector-with-graph-1","title":"Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings","date":"2022-10-30","arxiv_id":"2210.16848","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":1,"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 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) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/using-context-to-vector-with-graph-1#ran","syntology_url":"https://syntology.ai/paper/2210.16848","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.16848"}},"official":{"repos":["binbinjiang/context2vector"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/morphte-injecting-morphology-in-tensorized","slug":"morphte-injecting-morphology-in-tensorized","title":"MorphTE: Injecting Morphology in Tensorized Embeddings","date":"2022-10-27","arxiv_id":"2210.15379","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/morphte-injecting-morphology-in-tensorized#ran","syntology_url":"https://syntology.ai/paper/2210.15379","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.15379"}},"official":{"repos":["bigganbing/Fairseq_MorphTE"],"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/a-robust-bias-mitigation-procedure-based-on","slug":"a-robust-bias-mitigation-procedure-based-on","title":"A Robust Bias Mitigation Procedure Based on the Stereotype Content Model","date":"2022-10-26","arxiv_id":"2210.14552","repositories_listed":1,"syntology":null},{"url":"/paper/discovering-differences-in-the-representation","slug":"discovering-differences-in-the-representation","title":"Discovering Differences in the Representation of People using Contextualized Semantic Axes","date":"2022-10-21","arxiv_id":"2210.12170","repositories_listed":1,"syntology":null},{"url":"/paper/fine-mixing-mitigating-backdoors-in-fine","slug":"fine-mixing-mitigating-backdoors-in-fine","title":"Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models","date":"2022-10-18","arxiv_id":"2210.09545","repositories_listed":1,"syntology":{"n":19,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":10,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":19,"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) · 10 unverified","sample_list":"/paper/fine-mixing-mitigating-backdoors-in-fine#ran","syntology_url":"https://syntology.ai/paper/2210.09545","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.09545"}},"official":{"repos":["huggingface/pytorch-transformers"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":10,"ran_from_kinds":["official"]}}},{"url":"/paper/rapo-an-adaptive-ranking-paradigm-for","slug":"rapo-an-adaptive-ranking-paradigm-for","title":"RAPO: An Adaptive Ranking Paradigm for Bilingual Lexicon Induction","date":"2022-10-18","arxiv_id":"2210.09926","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":5,"n_ran_checked":5,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":6,"phrase":"6 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/rapo-an-adaptive-ranking-paradigm-for#ran","syntology_url":"https://syntology.ai/paper/2210.09926","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.09926"}},"official":{"repos":["jlfj345wf/rapo"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":5,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/domain-specific-word-embeddings-with","slug":"domain-specific-word-embeddings-with","title":"Domain-Specific Word Embeddings with Structure Prediction","date":"2022-10-06","arxiv_id":"2210.04962","repositories_listed":1,"syntology":null},{"url":"/paper/aligning-word-vectors-on-low-resource","slug":"aligning-word-vectors-on-low-resource","title":"Aligning Word Vectors on Low-Resource Languages with Wiktionary","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/cilex-an-investigation-of-context-information","slug":"cilex-an-investigation-of-context-information","title":"CILex: An Investigation of Context Information for Lexical Substitution Methods","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/one-word-two-sides-traces-of-stance-in","slug":"one-word-two-sides-traces-of-stance-in","title":"One Word, Two Sides: Traces of Stance in Contextualized Word Representations","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/homophone-reveals-the-truth-a-reality-check","slug":"homophone-reveals-the-truth-a-reality-check","title":"Homophone Reveals the Truth: A Reality Check for Speech2Vec","date":"2022-09-22","arxiv_id":"2209.10791","repositories_listed":1,"syntology":null},{"url":"/paper/learning-distinct-and-representative-modes","slug":"learning-distinct-and-representative-modes","title":"Learning Distinct and Representative Styles for Image Captioning","date":"2022-09-17","arxiv_id":"2209.08231","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/learning-distinct-and-representative-modes#ran","syntology_url":"https://syntology.ai/paper/2209.08231","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.08231"}},"official":{"repos":["bladewaltz1/modecap"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/unsupervised-lexical-substitution-with","slug":"unsupervised-lexical-substitution-with","title":"Unsupervised Lexical Substitution with Decontextualised Embeddings","date":"2022-09-17","arxiv_id":"2209.08236","repositories_listed":1,"syntology":null},{"url":"/paper/integrating-form-and-meaning-a-multi-task","slug":"integrating-form-and-meaning-a-multi-task","title":"Integrating Form and Meaning: A Multi-Task Learning Model for Acoustic Word Embeddings","date":"2022-09-14","arxiv_id":"2209.06633","repositories_listed":1,"syntology":null},{"url":"/paper/layer-or-representation-space-what-makes-bert","slug":"layer-or-representation-space-what-makes-bert","title":"Layer or Representation Space: What makes BERT-based Evaluation Metrics Robust?","date":"2022-09-06","arxiv_id":"2209.02317","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-aware-attentional-neural-network","slug":"knowledge-aware-attentional-neural-network","title":"Knowledge-aware attentional neural network for review-based movie recommendation with explanations","date":"2022-09-04","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/debiasing-word-embeddings-with-nonlinear","slug":"debiasing-word-embeddings-with-nonlinear","title":"Debiasing Word Embeddings with Nonlinear Geometry","date":"2022-08-29","arxiv_id":"2208.13899","repositories_listed":1,"syntology":null},{"url":"/paper/learning-dynamic-contextualised-word","slug":"learning-dynamic-contextualised-word","title":"Learning Dynamic Contextualised Word Embeddings via Template-based Temporal Adaptation","date":"2022-08-23","arxiv_id":"2208.10734","repositories_listed":1,"syntology":null},{"url":"/paper/knowing-where-and-what-unified-word-block","slug":"knowing-where-and-what-unified-word-block","title":"Knowing Where and What: Unified Word Block Pretraining for Document Understanding","date":"2022-07-28","arxiv_id":"2207.13979","repositories_listed":1,"syntology":null},{"url":"/paper/soundchoice-grapheme-to-phoneme-models-with","slug":"soundchoice-grapheme-to-phoneme-models-with","title":"SoundChoice: Grapheme-to-Phoneme Models with Semantic Disambiguation","date":"2022-07-27","arxiv_id":"2207.13703","repositories_listed":1,"syntology":null},{"url":"/paper/a-tool-to-overcome-technical-barriers-for","slug":"a-tool-to-overcome-technical-barriers-for","title":"A methodology to characterize bias and harmful stereotypes in natural language processing in Latin America","date":"2022-07-14","arxiv_id":"2207.06591","repositories_listed":1,"syntology":null},{"url":"/paper/a-comparative-study-on-word-embeddings-and","slug":"a-comparative-study-on-word-embeddings-and","title":"A Comparative Study on Word Embeddings and Social NLP Tasks","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/bl-research-at-semeval-2022-task-1-deep","slug":"bl-research-at-semeval-2022-task-1-deep","title":"BL.Research at SemEval-2022 Task 1: Deep networks for Reverse Dictionary using embeddings and LSTM autoencoders","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/clinical-flair-a-pre-trained-language-model","slug":"clinical-flair-a-pre-trained-language-model","title":"Clinical Flair: A Pre-Trained Language Model for Spanish Clinical Natural Language Processing","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/cross-language-transfer-of-high-quality","slug":"cross-language-transfer-of-high-quality","title":"Cross-Language Transfer of High-Quality Annotations: Combining Neural Machine Translation with Cross-Linguistic Span Alignment to Apply NER to Clinical Texts in a Low-Resource Language","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/edinburgh-at-semeval-2022-task-1-jointly","slug":"edinburgh-at-semeval-2022-task-1-jointly","title":"Edinburgh at SemEval-2022 Task 1: Jointly Fishing for Word Embeddings and Definitions","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/turkishdelightnlp-a-neural-turkish-nlp","slug":"turkishdelightnlp-a-neural-turkish-nlp","title":"TurkishDelightNLP: A Neural Turkish NLP Toolkit","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/language-with-vision-a-study-on-grounded-word","slug":"language-with-vision-a-study-on-grounded-word","title":"Language with Vision: a Study on Grounded Word and Sentence Embeddings","date":"2022-06-17","arxiv_id":"2206.08823","repositories_listed":1,"syntology":null},{"url":"/paper/niksss-at-hinglisheval-language-agnostic-bert","slug":"niksss-at-hinglisheval-language-agnostic-bert","title":"niksss at HinglishEval: Language-agnostic BERT-based Contextual Embeddings with Catboost for Quality Evaluation of the Low-Resource Synthetically Generated Code-Mixed Hinglish Text","date":"2022-06-17","arxiv_id":"2206.08910","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-object-goal-visual-navigation","slug":"zero-shot-object-goal-visual-navigation","title":"Zero-shot object goal visual navigation","date":"2022-06-15","arxiv_id":"2206.07423","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/zero-shot-object-goal-visual-navigation#ran","syntology_url":"https://syntology.ai/paper/2206.07423","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.07423"}},"official":{"repos":["pioneer-innovation/zero-shot-object-navigation"],"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/measuring-gender-bias-in-word-embeddings-of","slug":"measuring-gender-bias-in-word-embeddings-of","title":"Measuring Gender Bias in Word Embeddings of Gendered Languages Requires Disentangling Grammatical Gender Signals","date":"2022-06-03","arxiv_id":"2206.01691","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-deep-learning-with-embedded","slug":"enhancing-deep-learning-with-embedded","title":"Enhancing Deep Learning with Embedded Features for Arabic Named Entity Recognition","date":"2022-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/entity-resolution-with-hierarchical-graph","slug":"entity-resolution-with-hierarchical-graph","title":"Entity Resolution with Hierarchical Graph Attention Networks","date":"2022-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/metaphor-detection-for-low-resource-languages-1","slug":"metaphor-detection-for-low-resource-languages-1","title":"Metaphor Detection for Low Resource Languages: From Zero-Shot to Few-Shot Learning in Middle High German","date":"2022-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/semeval-2022-task-1-codwoe-comparing","slug":"semeval-2022-task-1-codwoe-comparing","title":"Semeval-2022 Task 1: CODWOE -- Comparing Dictionaries and Word Embeddings","date":"2022-05-27","arxiv_id":"2205.13858","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/semeval-2022-task-1-codwoe-comparing#ran","syntology_url":"https://syntology.ai/paper/2205.13858","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.13858"}},"official":{"repos":["timotheemickus/codwoe"],"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/leveraging-dependency-grammar-for-fine","slug":"leveraging-dependency-grammar-for-fine","title":"Leveraging Dependency Grammar for Fine-Grained Offensive Language Detection using Graph Convolutional Networks","date":"2022-05-26","arxiv_id":"2205.13164","repositories_listed":1,"syntology":null},{"url":"/paper/utilizing-language-image-pretraining-for","slug":"utilizing-language-image-pretraining-for","title":"Utilizing Language-Image Pretraining for Efficient and Robust Bilingual Word Alignment","date":"2022-05-23","arxiv_id":"2205.11616","repositories_listed":1,"syntology":null},{"url":"/paper/disentangling-visual-embeddings-for","slug":"disentangling-visual-embeddings-for","title":"Disentangling Visual Embeddings for Attributes and Objects","date":"2022-05-17","arxiv_id":"2205.08536","repositories_listed":1,"syntology":null},{"url":"/paper/recovering-private-text-in-federated-learning","slug":"recovering-private-text-in-federated-learning","title":"Recovering Private Text in Federated Learning of Language Models","date":"2022-05-17","arxiv_id":"2205.08514","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"5 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; 4 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/recovering-private-text-in-federated-learning#ran","syntology_url":"https://syntology.ai/paper/2205.08514","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.08514"}},"official":{"repos":["princeton-sysml/film"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/design-and-implementation-of-a-quantum-kernel","slug":"design-and-implementation-of-a-quantum-kernel","title":"Design and Implementation of a Quantum Kernel for Natural Language Processing","date":"2022-05-13","arxiv_id":"2205.06409","repositories_listed":1,"syntology":null},{"url":"/paper/irb-nlp-at-semeval-2022-task-1-exploring-the","slug":"irb-nlp-at-semeval-2022-task-1-exploring-the","title":"IRB-NLP at SemEval-2022 Task 1: Exploring the Relationship Between Words and Their Semantic Representations","date":"2022-05-13","arxiv_id":"2205.06840","repositories_listed":1,"syntology":null},{"url":"/paper/hyperbolic-relevance-matching-for-neural","slug":"hyperbolic-relevance-matching-for-neural","title":"Hyperbolic Relevance Matching for Neural Keyphrase Extraction","date":"2022-05-04","arxiv_id":"2205.02047","repositories_listed":1,"syntology":null},{"url":"/paper/word-tour-one-dimensional-word-embeddings-via","slug":"word-tour-one-dimensional-word-embeddings-via","title":"Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem","date":"2022-05-04","arxiv_id":"2205.01954","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/word-tour-one-dimensional-word-embeddings-via#ran","syntology_url":"https://syntology.ai/paper/2205.01954","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.01954"}},"official":{"repos":["joisino/wordtour"],"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/multi-task-text-classification-using-graph","slug":"multi-task-text-classification-using-graph","title":"Multi-Task Text Classification using Graph Convolutional Networks for Large-Scale Low Resource Language","date":"2022-05-02","arxiv_id":"2205.01204","repositories_listed":1,"syntology":null},{"url":"/paper/can-we-use-word-embeddings-for-enhancing","slug":"can-we-use-word-embeddings-for-enhancing","title":"Can We Use Word Embeddings for Enhancing Guarani-Spanish Machine Translation?","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/caveats-of-measuring-semantic-change-of","slug":"caveats-of-measuring-semantic-change-of","title":"Caveats of Measuring Semantic Change of Cognates and Borrowings using Multilingual Word Embeddings","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/improving-relation-extraction-through-syntax","slug":"improving-relation-extraction-through-syntax","title":"Improving Relation Extraction through Syntax-induced Pre-training with Dependency Masking","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/imputing-out-of-vocabulary-embeddings-with-1","slug":"imputing-out-of-vocabulary-embeddings-with-1","title":"Imputing Out-of-Vocabulary Embeddings with LOVE Makes LanguageModels Robust with Little Cost","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-and-evaluating-character-1","slug":"learning-and-evaluating-character-1","title":"Learning and Evaluating Character Representations in Novels","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-bias-reduced-word-embeddings-using","slug":"learning-bias-reduced-word-embeddings-using","title":"Learning Bias-reduced Word Embeddings Using Dictionary Definitions","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/lm-bff-ms-improving-few-shot-fine-tuning-of","slug":"lm-bff-ms-improving-few-shot-fine-tuning-of","title":"LM-BFF-MS: Improving Few-Shot Fine-tuning of Language Models based on Multiple Soft Demonstration Memory","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/roadblocks-in-gender-bias-measurement-for","slug":"roadblocks-in-gender-bias-measurement-for","title":"Roadblocks in Gender Bias Measurement for Diachronic Corpora","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-survey-on-sentence-embedding-models","slug":"a-survey-on-sentence-embedding-models","title":"A Survey on Sentence Embedding Models Performance for Patent Analysis","date":"2022-04-28","arxiv_id":"2206.02690","repositories_listed":1,"syntology":null},{"url":"/paper/approach-to-predicting-news-a-precise-multi","slug":"approach-to-predicting-news-a-precise-multi","title":"Approach to Predicting News -- A Precise Multi-LSTM Network With BERT","date":"2022-04-26","arxiv_id":"2204.12093","repositories_listed":1,"syntology":null},{"url":"/paper/from-hyperbolic-geometry-back-to-word","slug":"from-hyperbolic-geometry-back-to-word","title":"From Hyperbolic Geometry Back to Word Embeddings","date":"2022-04-26","arxiv_id":"2204.12481","repositories_listed":1,"syntology":null},{"url":"/paper/emotion-aware-transformer-encoder-for-1","slug":"emotion-aware-transformer-encoder-for-1","title":"Emotion-Aware Transformer Encoder for Empathetic Dialogue Generation","date":"2022-04-24","arxiv_id":"2204.11320","repositories_listed":1,"syntology":null},{"url":"/paper/is-neural-topic-modelling-better-than-1","slug":"is-neural-topic-modelling-better-than-1","title":"Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics","date":"2022-04-21","arxiv_id":"2204.09874","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/is-neural-topic-modelling-better-than-1#ran","syntology_url":"https://syntology.ai/paper/2204.09874","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.09874"}},"official":{"repos":["hyintell/topicx"],"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/multimodal-hate-speech-detection-from-bengali","slug":"multimodal-hate-speech-detection-from-bengali","title":"Multimodal Hate Speech Detection from Bengali Memes and Texts","date":"2022-04-19","arxiv_id":"2204.10196","repositories_listed":1,"syntology":null},{"url":"/paper/blcu-icall-at-semeval-2022-task-1-cross","slug":"blcu-icall-at-semeval-2022-task-1-cross","title":"BLCU-ICALL at SemEval-2022 Task 1: Cross-Attention Multitasking Framework for Definition Modeling","date":"2022-04-16","arxiv_id":"2204.07701","repositories_listed":1,"syntology":null},{"url":"/paper/word-embeddings-are-capable-of-capturing","slug":"word-embeddings-are-capable-of-capturing","title":"Word Embeddings Are Capable of Capturing Rhythmic Similarity of Words","date":"2022-04-11","arxiv_id":"2204.04833","repositories_listed":1,"syntology":null},{"url":"/paper/towards-better-chinese-centric-neural-machine","slug":"towards-better-chinese-centric-neural-machine","title":"Towards Better Chinese-centric Neural Machine Translation for Low-resource Languages","date":"2022-04-09","arxiv_id":"2204.04344","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-extraction-of-nested-entities-in","slug":"automatic-extraction-of-nested-entities-in","title":"Automatic Extraction of Nested Entities in Clinical Referrals in Spanish","date":"2022-04-07","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/detecting-unassimilated-borrowings-in-spanish-1","slug":"detecting-unassimilated-borrowings-in-spanish-1","title":"Detecting Unassimilated Borrowings in Spanish: An Annotated Corpus and Approaches to Modeling","date":"2022-03-30","arxiv_id":"2203.16169","repositories_listed":1,"syntology":null},{"url":"/paper/an-evaluation-dataset-for-legal-word","slug":"an-evaluation-dataset-for-legal-word","title":"An Evaluation Dataset for Legal Word Embedding: A Case Study On Chinese Codex","date":"2022-03-29","arxiv_id":"2203.15173","repositories_listed":1,"syntology":null},{"url":"/paper/vgse-visually-grounded-semantic-embeddings","slug":"vgse-visually-grounded-semantic-embeddings","title":"VGSE: Visually-Grounded Semantic Embeddings for Zero-Shot Learning","date":"2022-03-20","arxiv_id":"2203.10444","repositories_listed":1,"syntology":null},{"url":"/paper/improving-word-translation-via-two-stage-1","slug":"improving-word-translation-via-two-stage-1","title":"Improving Word Translation via Two-Stage Contrastive Learning","date":"2022-03-15","arxiv_id":"2203.08307","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":1,"n_ran_checked":2,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"phrase":"5 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/improving-word-translation-via-two-stage-1#ran","syntology_url":"https://syntology.ai/paper/2203.08307","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.08307"}},"official":{"repos":["cambridgeltl/contrastivebli"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/imputing-out-of-vocabulary-embeddings-with","slug":"imputing-out-of-vocabulary-embeddings-with","title":"Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little Cost","date":"2022-03-15","arxiv_id":"2203.07860","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":3,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":2,"n_no_contract":3,"n_pointer_only":0,"phrase":"6 ran (of which 3 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 2 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/imputing-out-of-vocabulary-embeddings-with#ran","syntology_url":"https://syntology.ai/paper/2203.07860","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.07860"}},"official":{"repos":["tigerchen52/love"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/sense-embeddings-are-also-biased-evaluating-1","slug":"sense-embeddings-are-also-biased-evaluating-1","title":"Sense Embeddings are also Biased--Evaluating Social Biases in Static and Contextualised Sense Embeddings","date":"2022-03-14","arxiv_id":"2203.07523","repositories_listed":1,"syntology":null}],"record_sha256":"534e8ecc8e47a16ac32487569e7b2f45b32232e289313d9841a30fe27e8c8636","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}