{"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/sentence/papers/17","list_of":"/task/sentence","task":"Sentence","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":17,"pages_in_order":108,"rows_per_page":100,"rows":[1601,1700],"of":10752,"counts":{"archive_papers_tagged":10752,"with_a_code_link":3811,"where_syntology_ran_a_sample":657,"not_listed_spam_title":0,"listed":10752,"listed_where_code_ran":657,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":544,"every_run_a_failure_of_syntologys_instrument":113,"listed_with_a_run_with_no_instrument_failure":544,"listed_every_run_a_failure_of_syntologys_instrument":113,"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/sentence","prev":"/task/sentence/papers/16","next":"/task/sentence/papers/18","papers":[{"url":"/paper/mt-geneval-a-counterfactual-and-contextual","slug":"mt-geneval-a-counterfactual-and-contextual","title":"MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation","date":"2022-11-02","arxiv_id":"2211.01355","repositories_listed":1,"syntology":null},{"url":"/paper/quantum-natural-language-generation-on-near","slug":"quantum-natural-language-generation-on-near","title":"Quantum Natural Language Generation on Near-Term Devices","date":"2022-11-01","arxiv_id":"2211.00727","repositories_listed":1,"syntology":null},{"url":"/paper/why-is-it-hate-speech-masked-rationale-1","slug":"why-is-it-hate-speech-masked-rationale-1","title":"Why Is It Hate Speech? Masked Rationale Prediction for Explainable Hate Speech Detection","date":"2022-11-01","arxiv_id":"2211.00243","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/why-is-it-hate-speech-masked-rationale-1#ran","syntology_url":"https://syntology.ai/paper/2211.00243","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.00243"}},"official":{"repos":["alatteaday/mrp_hate-speech-detection"],"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/1cademy-causal-news-corpus-2022-enhance","slug":"1cademy-causal-news-corpus-2022-enhance","title":"1Cademy @ Causal News Corpus 2022: Enhance Causal Span Detection via Beam-Search-based Position Selector","date":"2022-10-31","arxiv_id":"2210.17157","repositories_listed":1,"syntology":null},{"url":"/paper/character-level-white-box-adversarial-attacks","slug":"character-level-white-box-adversarial-attacks","title":"Character-level White-Box Adversarial Attacks against Transformers via Attachable Subwords Substitution","date":"2022-10-31","arxiv_id":"2210.17004","repositories_listed":1,"syntology":null},{"url":"/paper/max-pooling-with-vision-transformers","slug":"max-pooling-with-vision-transformers","title":"Max Pooling with Vision Transformers reconciles class and shape in weakly supervised semantic segmentation","date":"2022-10-31","arxiv_id":"2210.17400","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/max-pooling-with-vision-transformers#ran","syntology_url":"https://syntology.ai/paper/2210.17400","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.17400"}},"official":{"repos":["deepplants/vit-pcm"],"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/rlet-a-reinforcement-learning-based-approach","slug":"rlet-a-reinforcement-learning-based-approach","title":"RLET: A Reinforcement Learning Based Approach for Explainable QA with Entailment Trees","date":"2022-10-31","arxiv_id":"2210.17095","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-novelty-detection-and","slug":"semantic-novelty-detection-and","title":"Semantic Novelty Detection and Characterization in Factual Text Involving Named Entities","date":"2022-10-31","arxiv_id":"2210.17440","repositories_listed":1,"syntology":null},{"url":"/paper/generate-discriminate-and-contrast-a-semi","slug":"generate-discriminate-and-contrast-a-semi","title":"Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework","date":"2022-10-30","arxiv_id":"2210.16798","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/generate-discriminate-and-contrast-a-semi#ran","syntology_url":"https://syntology.ai/paper/2210.16798","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.16798"}},"official":{"repos":["matthewcym/gense"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text"]}}},{"url":"/paper/improved-acoustic-to-articulatory-inversion","slug":"improved-acoustic-to-articulatory-inversion","title":"Improved acoustic-to-articulatory inversion using representations from pretrained self-supervised learning models","date":"2022-10-30","arxiv_id":"2210.16871","repositories_listed":1,"syntology":null},{"url":"/paper/beyond-prompting-making-pre-trained-language","slug":"beyond-prompting-making-pre-trained-language","title":"Beyond Prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations","date":"2022-10-29","arxiv_id":"2210.16637","repositories_listed":1,"syntology":null},{"url":"/paper/differentiable-data-augmentation-for","slug":"differentiable-data-augmentation-for","title":"Differentiable Data Augmentation for Contrastive Sentence Representation Learning","date":"2022-10-29","arxiv_id":"2210.16536","repositories_listed":1,"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/differentiable-data-augmentation-for#ran","syntology_url":"https://syntology.ai/paper/2210.16536","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.16536"}},"official":{"repos":["tianduowang/diffaug"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/exploiting-prompt-learning-with-pre-trained","slug":"exploiting-prompt-learning-with-pre-trained","title":"Exploiting prompt learning with pre-trained language models for Alzheimer's Disease detection","date":"2022-10-29","arxiv_id":"2210.16539","repositories_listed":1,"syntology":null},{"url":"/paper/two-is-better-than-many-binary-classification","slug":"two-is-better-than-many-binary-classification","title":"Two is Better than Many? Binary Classification as an Effective Approach to Multi-Choice Question Answering","date":"2022-10-29","arxiv_id":"2210.16495","repositories_listed":1,"syntology":{"n":7,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":5,"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) · 5 unverified","sample_list":"/paper/two-is-better-than-many-binary-classification#ran","syntology_url":"https://syntology.ai/paper/2210.16495","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.16495"}},"official":{"repos":["declare-lab/team"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/toward-unifying-text-segmentation-and-long","slug":"toward-unifying-text-segmentation-and-long","title":"Toward Unifying Text Segmentation and Long Document Summarization","date":"2022-10-28","arxiv_id":"2210.16422","repositories_listed":1,"syntology":null},{"url":"/paper/dial2vec-self-guided-contrastive-learning-of","slug":"dial2vec-self-guided-contrastive-learning-of","title":"Dial2vec: Self-Guided Contrastive Learning of Unsupervised Dialogue Embeddings","date":"2022-10-27","arxiv_id":"2210.15332","repositories_listed":1,"syntology":{"n":11,"n_ran":7,"n_constructed":3,"n_ran_checked":7,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 3 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/dial2vec-self-guided-contrastive-learning-of#ran","syntology_url":"https://syntology.ai/paper/2210.15332","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.15332"}},"official":{"repos":["alibabaresearch/damo-convai"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":3,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/tasa-deceiving-question-answering-models-by","slug":"tasa-deceiving-question-answering-models-by","title":"TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack","date":"2022-10-27","arxiv_id":"2210.15221","repositories_listed":1,"syntology":null},{"url":"/paper/towards-better-text-image-consistency-in-text","slug":"towards-better-text-image-consistency-in-text","title":"SSD: Towards Better Text-Image Consistency Metric in Text-to-Image Generation","date":"2022-10-27","arxiv_id":"2210.15235","repositories_listed":1,"syntology":null},{"url":"/paper/a-bilingual-parallel-corpus-with-discourse","slug":"a-bilingual-parallel-corpus-with-discourse","title":"A Bilingual Parallel Corpus with Discourse Annotations","date":"2022-10-26","arxiv_id":"2210.14667","repositories_listed":1,"syntology":null},{"url":"/paper/is-multiwoz-a-solved-task-an-interactive-tod","slug":"is-multiwoz-a-solved-task-an-interactive-tod","title":"Is MultiWOZ a Solved Task? An Interactive TOD Evaluation Framework with User Simulator","date":"2022-10-26","arxiv_id":"2210.14529","repositories_listed":1,"syntology":null},{"url":"/paper/sentbs-sentence-level-beam-search-for","slug":"sentbs-sentence-level-beam-search-for","title":"SentBS: Sentence-level Beam Search for Controllable Summarization","date":"2022-10-26","arxiv_id":"2210.14502","repositories_listed":1,"syntology":null},{"url":"/paper/there-is-more-than-one-kind-of-robustness","slug":"there-is-more-than-one-kind-of-robustness","title":"There is more than one kind of robustness: Fooling Whisper with adversarial examples","date":"2022-10-26","arxiv_id":"2210.17316","repositories_listed":1,"syntology":null},{"url":"/paper/help-me-write-a-poem-instruction-tuning-as-a","slug":"help-me-write-a-poem-instruction-tuning-as-a","title":"Help me write a poem: Instruction Tuning as a Vehicle for Collaborative Poetry Writing","date":"2022-10-25","arxiv_id":"2210.13669","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/help-me-write-a-poem-instruction-tuning-as-a#ran","syntology_url":"https://syntology.ai/paper/2210.13669","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.13669"}},"official":{"repos":["vishakhpk/creative-instructions"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/focused-concatenation-for-context-aware","slug":"focused-concatenation-for-context-aware","title":"Focused Concatenation for Context-Aware Neural Machine Translation","date":"2022-10-24","arxiv_id":"2210.13388","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":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) · 0 unverified","sample_list":"/paper/focused-concatenation-for-context-aware#ran","syntology_url":"https://syntology.ai/paper/2210.13388","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.13388"}},"official":{"repos":["lorelupo/focused-concat"],"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/subspace-based-set-operations-on-a-pre","slug":"subspace-based-set-operations-on-a-pre","title":"Subspace Representations for Soft Set Operations and Sentence Similarities","date":"2022-10-24","arxiv_id":"2210.13034","repositories_listed":1,"syntology":null},{"url":"/paper/bootstrapping-meaning-through-listening","slug":"bootstrapping-meaning-through-listening","title":"Bootstrapping meaning through listening: Unsupervised learning of spoken sentence embeddings","date":"2022-10-23","arxiv_id":"2210.12857","repositories_listed":1,"syntology":null},{"url":"/paper/language-model-pre-training-with-sparse","slug":"language-model-pre-training-with-sparse","title":"Language Model Pre-Training with Sparse Latent Typing","date":"2022-10-23","arxiv_id":"2210.12582","repositories_listed":1,"syntology":{"n":12,"n_ran":12,"n_constructed":0,"n_ran_checked":9,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":3,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/language-model-pre-training-with-sparse#ran","syntology_url":"https://syntology.ai/paper/2210.12582","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12582"}},"official":{"repos":["renll/sparselt"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/tail-batch-sampling-approximating-global","slug":"tail-batch-sampling-approximating-global","title":"Global Contrastive Batch Sampling via Optimization on Sample Permutations","date":"2022-10-23","arxiv_id":"2210.12874","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/tail-batch-sampling-approximating-global#ran","syntology_url":"https://syntology.ai/paper/2210.12874","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12874"}},"official":{"repos":["vinayak1/gcbs"],"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/the-curious-case-of-absolute-position","slug":"the-curious-case-of-absolute-position","title":"The Curious Case of Absolute Position Embeddings","date":"2022-10-23","arxiv_id":"2210.12574","repositories_listed":1,"syntology":null},{"url":"/paper/translation-word-level-auto-completion-what","slug":"translation-word-level-auto-completion-what","title":"Translation Word-Level Auto-Completion: What can we achieve out of the box?","date":"2022-10-23","arxiv_id":"2210.12802","repositories_listed":1,"syntology":null},{"url":"/paper/ham-hierarchical-attention-model-with-high","slug":"ham-hierarchical-attention-model-with-high","title":"Learning Point-Language Hierarchical Alignment for 3D Visual Grounding","date":"2022-10-22","arxiv_id":"2210.12513","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/ham-hierarchical-attention-model-with-high#ran","syntology_url":"https://syntology.ai/paper/2210.12513","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12513"}},"official":{"repos":["ppjmchen/ham"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/pcmsp-a-dataset-for-scientific-action-graphs","slug":"pcmsp-a-dataset-for-scientific-action-graphs","title":"PcMSP: A Dataset for Scientific Action Graphs Extraction from Polycrystalline Materials Synthesis Procedure Text","date":"2022-10-22","arxiv_id":"2210.12401","repositories_listed":1,"syntology":null},{"url":"/paper/r-2-f-a-general-retrieval-reading-and-fusion","slug":"r-2-f-a-general-retrieval-reading-and-fusion","title":"R$^2$F: A General Retrieval, Reading and Fusion Framework for Document-level Natural Language Inference","date":"2022-10-22","arxiv_id":"2210.12328","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/r-2-f-a-general-retrieval-reading-and-fusion#ran","syntology_url":"https://syntology.ai/paper/2210.12328","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12328"}},"official":{"repos":["phoenixsecularbird/r2f"],"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/weakly-supervised-temporal-article-grounding","slug":"weakly-supervised-temporal-article-grounding","title":"Weakly-Supervised Temporal Article Grounding","date":"2022-10-22","arxiv_id":"2210.12444","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/weakly-supervised-temporal-article-grounding#ran","syntology_url":"https://syntology.ai/paper/2210.12444","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12444"}},"official":{"repos":["zjuchenlong/wsag"],"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/a-dataset-for-plain-language-adaptation-of","slug":"a-dataset-for-plain-language-adaptation-of","title":"A Dataset for Plain Language Adaptation of Biomedical Abstracts","date":"2022-10-21","arxiv_id":"2210.12242","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-dataset-for-plain-language-adaptation-of#ran","syntology_url":"https://syntology.ai/paper/2210.12242","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12242"}},"official":{"repos":["attal-kush/PLABA"],"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/a-textless-metric-for-speech-to-speech","slug":"a-textless-metric-for-speech-to-speech","title":"A Textless Metric for Speech-to-Speech Comparison","date":"2022-10-21","arxiv_id":"2210.11835","repositories_listed":1,"syntology":null},{"url":"/paper/cefr-based-sentence-difficulty-annotation-and","slug":"cefr-based-sentence-difficulty-annotation-and","title":"CEFR-Based Sentence Difficulty Annotation and Assessment","date":"2022-10-21","arxiv_id":"2210.11766","repositories_listed":1,"syntology":{"n":8,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":8,"phrase":"3 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; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/cefr-based-sentence-difficulty-annotation-and#ran","syntology_url":"https://syntology.ai/paper/2210.11766","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.11766"}},"official":{"repos":["yukiar/cefr-sp"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/modeling-document-level-temporal-structures","slug":"modeling-document-level-temporal-structures","title":"Modeling Document-level Temporal Structures for Building Temporal Dependency Graphs","date":"2022-10-21","arxiv_id":"2210.11787","repositories_listed":1,"syntology":null},{"url":"/paper/probing-with-noise-unpicking-the-warp-and","slug":"probing-with-noise-unpicking-the-warp-and","title":"Probing with Noise: Unpicking the Warp and Weft of Embeddings","date":"2022-10-21","arxiv_id":"2210.12206","repositories_listed":1,"syntology":null},{"url":"/paper/sling-sino-linguistic-evaluation-of-large","slug":"sling-sino-linguistic-evaluation-of-large","title":"SLING: Sino Linguistic Evaluation of Large Language Models","date":"2022-10-21","arxiv_id":"2210.11689","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/sling-sino-linguistic-evaluation-of-large#ran","syntology_url":"https://syntology.ai/paper/2210.11689","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.11689"}},"official":{"repos":["yixiao-song/sling_data_code"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/turning-fixed-to-adaptive-integrating-post","slug":"turning-fixed-to-adaptive-integrating-post","title":"Turning Fixed to Adaptive: Integrating Post-Evaluation into Simultaneous Machine Translation","date":"2022-10-21","arxiv_id":"2210.11900","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":0,"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/turning-fixed-to-adaptive-integrating-post#ran","syntology_url":"https://syntology.ai/paper/2210.11900","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.11900"}},"official":{"repos":["ictnlp/ped-simt"],"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/augcse-contrastive-sentence-embedding-with","slug":"augcse-contrastive-sentence-embedding-with","title":"AugCSE: Contrastive Sentence Embedding with Diverse Augmentations","date":"2022-10-20","arxiv_id":"2210.13749","repositories_listed":1,"syntology":null},{"url":"/paper/finding-dataset-shortcuts-with-grammar","slug":"finding-dataset-shortcuts-with-grammar","title":"Finding Dataset Shortcuts with Grammar Induction","date":"2022-10-20","arxiv_id":"2210.11560","repositories_listed":1,"syntology":null},{"url":"/paper/improving-aspect-sentiment-quad-prediction","slug":"improving-aspect-sentiment-quad-prediction","title":"Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentation","date":"2022-10-19","arxiv_id":"2210.10291","repositories_listed":1,"syntology":null},{"url":"/paper/alibaba-translate-china-s-submission-for-wmt-1","slug":"alibaba-translate-china-s-submission-for-wmt-1","title":"Alibaba-Translate China's Submission for WMT 2022 Quality Estimation Shared Task","date":"2022-10-18","arxiv_id":"2210.10049","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/graph-based-multilingual-label-propagation","slug":"graph-based-multilingual-label-propagation","title":"Graph-Based Multilingual Label Propagation for Low-Resource Part-of-Speech Tagging","date":"2022-10-18","arxiv_id":"2210.09840","repositories_listed":1,"syntology":null},{"url":"/paper/retrofitting-multilingual-sentence-embeddings","slug":"retrofitting-multilingual-sentence-embeddings","title":"Retrofitting Multilingual Sentence Embeddings with Abstract Meaning Representation","date":"2022-10-18","arxiv_id":"2210.09773","repositories_listed":1,"syntology":null},{"url":"/paper/sentiment-aware-word-and-sentence-level-pre","slug":"sentiment-aware-word-and-sentence-level-pre","title":"Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis","date":"2022-10-18","arxiv_id":"2210.09803","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/sentiment-aware-word-and-sentence-level-pre#ran","syntology_url":"https://syntology.ai/paper/2210.09803","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.09803"}},"official":{"repos":["xmudm/sentiwsp"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/synergy-with-translation-artifacts-for","slug":"synergy-with-translation-artifacts-for","title":"Synergy with Translation Artifacts for Training and Inference in Multilingual Tasks","date":"2022-10-18","arxiv_id":"2210.09588","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/synergy-with-translation-artifacts-for#ran","syntology_url":"https://syntology.ai/paper/2210.09588","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.09588"}},"official":{"repos":["jongwooko/musc"],"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/effective-and-efficient-query-aware-snippet","slug":"effective-and-efficient-query-aware-snippet","title":"Effective and Efficient Query-aware Snippet Extraction for Web Search","date":"2022-10-17","arxiv_id":"2210.08809","repositories_listed":1,"syntology":null},{"url":"/paper/cdconv-a-benchmark-for-contradiction","slug":"cdconv-a-benchmark-for-contradiction","title":"CDConv: A Benchmark for Contradiction Detection in Chinese Conversations","date":"2022-10-16","arxiv_id":"2210.08511","repositories_listed":1,"syntology":null},{"url":"/paper/modeling-context-with-linear-attention-for-1","slug":"modeling-context-with-linear-attention-for-1","title":"Modeling Context With Linear Attention for Scalable Document-Level Translation","date":"2022-10-16","arxiv_id":"2210.08431","repositories_listed":1,"syntology":null},{"url":"/paper/sentence-representation-learning-with","slug":"sentence-representation-learning-with","title":"Sentence Representation Learning with Generative Objective rather than Contrastive Objective","date":"2022-10-16","arxiv_id":"2210.08474","repositories_listed":1,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 3 unverified","sample_list":"/paper/sentence-representation-learning-with#ran","syntology_url":"https://syntology.ai/paper/2210.08474","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.08474"}},"official":{"repos":["chengzhipanpan/paser"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"url":"/paper/conentail-an-entailment-based-framework-for","slug":"conentail-an-entailment-based-framework-for","title":"ConEntail: An Entailment-based Framework for Universal Zero and Few Shot Classification with Supervised Contrastive Pretraining","date":"2022-10-14","arxiv_id":"2210.07587","repositories_listed":1,"syntology":null},{"url":"/paper/holistic-sentence-embeddings-for-better-out","slug":"holistic-sentence-embeddings-for-better-out","title":"Holistic Sentence Embeddings for Better Out-of-Distribution Detection","date":"2022-10-14","arxiv_id":"2210.07485","repositories_listed":1,"syntology":null},{"url":"/paper/stylex-explaining-styles-with-lexicon-based","slug":"stylex-explaining-styles-with-lexicon-based","title":"StyLEx: Explaining Style Using Human Lexical Annotations","date":"2022-10-14","arxiv_id":"2210.07469","repositories_listed":1,"syntology":null},{"url":"/paper/transfusion-transcribing-speech-with","slug":"transfusion-transcribing-speech-with","title":"TransFusion: Transcribing Speech with Multinomial Diffusion","date":"2022-10-14","arxiv_id":"2210.07677","repositories_listed":1,"syntology":null},{"url":"/paper/crop-zero-shot-cross-lingual-named-entity","slug":"crop-zero-shot-cross-lingual-named-entity","title":"CROP: Zero-shot Cross-lingual Named Entity Recognition with Multilingual Labeled Sequence Translation","date":"2022-10-13","arxiv_id":"2210.07022","repositories_listed":1,"syntology":null},{"url":"/paper/low-resource-neural-machine-translation-with","slug":"low-resource-neural-machine-translation-with","title":"Low-resource Neural Machine Translation with Cross-modal Alignment","date":"2022-10-13","arxiv_id":"2210.06716","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/low-resource-neural-machine-translation-with#ran","syntology_url":"https://syntology.ai/paper/2210.06716","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.06716"}},"official":{"repos":["ictnlp/lnmt-ca"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/sentence-ambiguity-grammaticality-and","slug":"sentence-ambiguity-grammaticality-and","title":"Sentence Ambiguity, Grammaticality and Complexity Probes","date":"2022-10-13","arxiv_id":"2210.06928","repositories_listed":1,"syntology":null},{"url":"/paper/better-smatch-better-parser-amr-evaluation-is","slug":"better-smatch-better-parser-amr-evaluation-is","title":"Better Smatch = Better Parser? AMR evaluation is not so simple anymore","date":"2022-10-12","arxiv_id":"2210.06461","repositories_listed":1,"syntology":null},{"url":"/paper/discourse-analysis-via-questions-and-answers","slug":"discourse-analysis-via-questions-and-answers","title":"Discourse Analysis via Questions and Answers: Parsing Dependency Structures of Questions Under Discussion","date":"2022-10-12","arxiv_id":"2210.05905","repositories_listed":1,"syntology":null},{"url":"/paper/eduqg-a-multi-format-multiple-choice-dataset","slug":"eduqg-a-multi-format-multiple-choice-dataset","title":"EduQG: A Multi-format Multiple Choice Dataset for the Educational Domain","date":"2022-10-12","arxiv_id":"2210.06104","repositories_listed":1,"syntology":null},{"url":"/paper/language-agnostic-multilingual-information","slug":"language-agnostic-multilingual-information","title":"Language Agnostic Multilingual Information Retrieval with Contrastive Learning","date":"2022-10-12","arxiv_id":"2210.06633","repositories_listed":1,"syntology":null},{"url":"/paper/primesrl-eval-a-practical-quality-metric-for","slug":"primesrl-eval-a-practical-quality-metric-for","title":"PriMeSRL-Eval: A Practical Quality Metric for Semantic Role Labeling Systems Evaluation","date":"2022-10-12","arxiv_id":"2210.06408","repositories_listed":1,"syntology":null},{"url":"/paper/capturing-global-structural-information-in","slug":"capturing-global-structural-information-in","title":"Capturing Global Structural Information in Long Document Question Answering with Compressive Graph Selector Network","date":"2022-10-11","arxiv_id":"2210.05499","repositories_listed":1,"syntology":null},{"url":"/paper/once-is-enough-a-light-weight-cross-attention","slug":"once-is-enough-a-light-weight-cross-attention","title":"Once is Enough: A Light-Weight Cross-Attention for Fast Sentence Pair Modeling","date":"2022-10-11","arxiv_id":"2210.05261","repositories_listed":1,"syntology":null},{"url":"/paper/mmt-image-guided-story-ending-generation-with","slug":"mmt-image-guided-story-ending-generation-with","title":"MMT: Image-guided Story Ending Generation with Multimodal Memory Transformer","date":"2022-10-10","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/robustification-of-multilingual-language","slug":"robustification-of-multilingual-language","title":"Robustification of Multilingual Language Models to Real-world Noise in Crosslingual Zero-shot Settings with Robust Contrastive Pretraining","date":"2022-10-10","arxiv_id":"2210.04782","repositories_listed":1,"syntology":null},{"url":"/paper/asdot-any-shot-data-to-text-generation-with","slug":"asdot-any-shot-data-to-text-generation-with","title":"ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Models","date":"2022-10-09","arxiv_id":"2210.04325","repositories_listed":1,"syntology":null},{"url":"/paper/contra-con-text-tra-nsformer-for-cross-modal","slug":"contra-con-text-tra-nsformer-for-cross-modal","title":"ConTra: (Con)text (Tra)nsformer for Cross-Modal Video Retrieval","date":"2022-10-09","arxiv_id":"2210.04341","repositories_listed":1,"syntology":null},{"url":"/paper/cross-align-modeling-deep-cross-lingual","slug":"cross-align-modeling-deep-cross-lingual","title":"Cross-Align: Modeling Deep Cross-lingual Interactions for Word Alignment","date":"2022-10-09","arxiv_id":"2210.04141","repositories_listed":1,"syntology":null},{"url":"/paper/hegel-hypergraph-transformer-for-long","slug":"hegel-hypergraph-transformer-for-long","title":"HEGEL: Hypergraph Transformer for Long Document Summarization","date":"2022-10-09","arxiv_id":"2210.04126","repositories_listed":1,"syntology":null},{"url":"/paper/arabsign-a-multi-modality-dataset-and","slug":"arabsign-a-multi-modality-dataset-and","title":"ArabSign: A Multi-modality Dataset and Benchmark for Continuous Arabic Sign Language Recognition","date":"2022-10-08","arxiv_id":"2210.03951","repositories_listed":1,"syntology":null},{"url":"/paper/kalm-knowledge-aware-integration-of-local","slug":"kalm-knowledge-aware-integration-of-local","title":"KALM: Knowledge-Aware Integration of Local, Document, and Global Contexts for Long Document Understanding","date":"2022-10-08","arxiv_id":"2210.04105","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":1,"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: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/kalm-knowledge-aware-integration-of-local#ran","syntology_url":"https://syntology.ai/paper/2210.04105","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.04105"}},"official":{"repos":["bunsenfeng/kalm"],"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/sda-simple-discrete-augmentation-for","slug":"sda-simple-discrete-augmentation-for","title":"SDA: Simple Discrete Augmentation for Contrastive Sentence Representation Learning","date":"2022-10-08","arxiv_id":"2210.03963","repositories_listed":1,"syntology":null},{"url":"/paper/nmtsloth-understanding-and-testing-efficiency","slug":"nmtsloth-understanding-and-testing-efficiency","title":"LLMEffiChecker: Understanding and Testing Efficiency Degradation of Large Language Models","date":"2022-10-07","arxiv_id":"2210.03696","repositories_listed":1,"syntology":null},{"url":"/paper/uu-tax-at-semeval-2022-task-3-improving-the-1","slug":"uu-tax-at-semeval-2022-task-3-improving-the-1","title":"UU-Tax at SemEval-2022 Task 3: Improving the generalizability of language models for taxonomy classification through data augmentation","date":"2022-10-07","arxiv_id":"2210.03378","repositories_listed":1,"syntology":null},{"url":"/paper/augmentor-or-filter-reconsider-the-role-of","slug":"augmentor-or-filter-reconsider-the-role-of","title":"BootAug: Boosting Text Augmentation via Hybrid Instance Filtering Framework","date":"2022-10-06","arxiv_id":"2210.02941","repositories_listed":1,"syntology":null},{"url":"/paper/improving-the-domain-adaptation-of-retrieval","slug":"improving-the-domain-adaptation-of-retrieval","title":"Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering","date":"2022-10-06","arxiv_id":"2210.02627","repositories_listed":1,"syntology":null},{"url":"/paper/learning-functional-sections-in-medical","slug":"learning-functional-sections-in-medical","title":"Learning functional sections in medical conversations: iterative pseudo-labeling and human-in-the-loop approach","date":"2022-10-06","arxiv_id":"2210.02658","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-sentence-textual-similarity-with","slug":"unsupervised-sentence-textual-similarity-with","title":"Unsupervised Sentence Textual Similarity with Compositional Phrase Semantics","date":"2022-10-05","arxiv_id":"2210.02284","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-collocate-visual-linguistic","slug":"learning-to-collocate-visual-linguistic","title":"Learning to Collocate Visual-Linguistic Neural Modules for Image Captioning","date":"2022-10-04","arxiv_id":"2210.01338","repositories_listed":1,"syntology":null},{"url":"/paper/a-dual-attention-learning-network-with-word","slug":"a-dual-attention-learning-network-with-word","title":"A Dual-Attention Learning Network with Word and Sentence Embedding for Medical Visual Question Answering","date":"2022-10-01","arxiv_id":"2210.00220","repositories_listed":1,"syntology":null},{"url":"/paper/a-structure-aware-argument-encoder-for-1","slug":"a-structure-aware-argument-encoder-for-1","title":"A Structure-Aware Argument Encoder for Literature Discourse Analysis","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/accounting-for-language-effect-in-the","slug":"accounting-for-language-effect-in-the","title":"Accounting for Language Effect in the Evaluation of Cross-lingual AMR Parsers","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/boundary-detection-and-categorization-of","slug":"boundary-detection-and-categorization-of","title":"Boundary Detection and Categorization of Argument Aspects via Supervised Learning","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/cctc-a-cross-sentence-chinese-text-correction","slug":"cctc-a-cross-sentence-chinese-text-correction","title":"CCTC: A Cross-Sentence Chinese Text Correction Dataset for Native Speakers","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/ceta-a-consensus-enhanced-training-approach","slug":"ceta-a-consensus-enhanced-training-approach","title":"CETA: A Consensus Enhanced Training Approach for Denoising in Distantly Supervised Relation Extraction","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/codonmt-modeling-cohesion-devices-for","slug":"codonmt-modeling-cohesion-devices-for","title":"CoDoNMT: Modeling Cohesion Devices for Document-Level Neural Machine Translation","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/composition-based-heterogeneous-graph-multi","slug":"composition-based-heterogeneous-graph-multi","title":"Composition-based Heterogeneous Graph Multi-channel Attention Network for Multi-aspect Multi-sentiment Classification","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/dependency-aware-prototype-learning-for-few","slug":"dependency-aware-prototype-learning-for-few","title":"Dependency-aware Prototype Learning for Few-shot Relation Classification","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/desed-dialogue-based-explanation-for-sentence","slug":"desed-dialogue-based-explanation-for-sentence","title":"DESED: Dialogue-based Explanation for Sentence-level Event Detection","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/dont-judge-a-language-model-by-its-last-layer","slug":"dont-judge-a-language-model-by-its-last-layer","title":"Don’t Judge a Language Model by Its Last Layer: Contrastive Learning with Layer-Wise Attention Pooling","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-structure-aware-encoder-with","slug":"enhancing-structure-aware-encoder-with","title":"Enhancing Structure-aware Encoder with Extremely Limited Data for Graph-based Dependency Parsing","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/event-causality-identification-via-derivative","slug":"event-causality-identification-via-derivative","title":"Event Causality Identification via Derivative Prompt Joint Learning","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/gusum-graph-based-unsupervised-summarization-1","slug":"gusum-graph-based-unsupervised-summarization-1","title":"GUSUM: Graph-based Unsupervised Summarization Using Sentence Features Scoring and Sentence-BERT","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/how-about-time-probing-a-multilingual","slug":"how-about-time-probing-a-multilingual","title":"How about Time? Probing a Multilingual Language Model for Temporal Relations","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null}],"record_sha256":"6d022f1d6ee6673c590174086bafc63b6b86cef5ad53191d0d21fd07b56e785a","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}