{"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":"/method/weight-decay/papers/96","list_of":"/method/weight-decay","method":"Weight Decay","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":96,"pages_in_order":108,"rows_per_page":100,"rows":[9501,9600],"of":10713,"counts":{"archive_papers_tagged":10713,"with_a_code_link":4533,"where_syntology_ran_a_sample":1291,"not_listed_spam_title":0,"listed":10713,"listed_where_code_ran":1291,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1064,"every_run_a_failure_of_syntologys_instrument":227,"listed_with_a_run_with_no_instrument_failure":1064,"listed_every_run_a_failure_of_syntologys_instrument":227,"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":"/method/weight-decay","prev":"/method/weight-decay/papers/95","next":"/method/weight-decay/papers/97","papers":[{"paper":"/paper/fashionbert-text-and-image-matching-with","slug":"fashionbert-text-and-image-matching-with","title":"FashionBERT: Text and Image Matching with Adaptive Loss for Cross-modal Retrieval","date":"2020-05-20","arxiv_id":"2005.09801","n_code_links":3,"syntology":null},{"paper":null,"slug":"cross-lingual-transfer-learning-for-dialogue","title":"Cross-lingual Approaches for Task-specific Dialogue Act Recognition","date":"2020-05-19","arxiv_id":"2005.09260","n_code_links":0,"syntology":null},{"paper":null,"slug":"experience-augmentation-boosting-and","title":"Experience Augmentation: Boosting and Accelerating Off-Policy Multi-Agent Reinforcement Learning","date":"2020-05-19","arxiv_id":"2005.09453","n_code_links":0,"syntology":null},{"paper":"/paper/sketch-bert-learning-sketch-bidirectional","slug":"sketch-bert-learning-sketch-bidirectional","title":"Sketch-BERT: Learning Sketch Bidirectional Encoder Representation from Transformers by Self-supervised Learning of Sketch Gestalt","date":"2020-05-19","arxiv_id":"2005.09159","n_code_links":1,"syntology":null},{"paper":"/paper/table-search-using-a-deep-contextualized","slug":"table-search-using-a-deep-contextualized","title":"Table Search Using a Deep Contextualized Language Model","date":"2020-05-19","arxiv_id":"2005.09207","n_code_links":1,"syntology":null},{"paper":"/paper/are-all-languages-created-equal-in","slug":"are-all-languages-created-equal-in","title":"Are All Languages Created Equal in Multilingual BERT?","date":"2020-05-18","arxiv_id":"2005.09093","n_code_links":1,"syntology":null},{"paper":null,"slug":"semeval-2020-task-5-detecting-counterfactuals","title":"Yseop at SemEval-2020 Task 5: Cascaded BERT Language Model for Counterfactual Statement Analysis","date":"2020-05-18","arxiv_id":"2005.08519","n_code_links":0,"syntology":null},{"paper":"/paper/adversarial-training-for-commonsense","slug":"adversarial-training-for-commonsense","title":"Adversarial Training for Commonsense Inference","date":"2020-05-17","arxiv_id":"2005.08156","n_code_links":1,"syntology":null},{"paper":"/paper/building-a-hebrew-semantic-role-labeling","slug":"building-a-hebrew-semantic-role-labeling","title":"Building a Hebrew Semantic Role Labeling Lexical Resource from Parallel Movie Subtitles","date":"2020-05-17","arxiv_id":"2005.08206","n_code_links":1,"syntology":null},{"paper":null,"slug":"context-based-quotation-recommendation","title":"Context-Based Quotation Recommendation","date":"2020-05-17","arxiv_id":"2005.08319","n_code_links":0,"syntology":null},{"paper":"/paper/cross-lingual-low-resource-set-to-description","slug":"cross-lingual-low-resource-set-to-description","title":"Cross-Lingual Low-Resource Set-to-Description Retrieval for Global E-Commerce","date":"2020-05-17","arxiv_id":"2005.08188","n_code_links":1,"syntology":null},{"paper":null,"slug":"support-bert-predicting-quality-of-question","title":"Support-BERT: Predicting Quality of Question-Answer Pairs in MSDN using Deep Bidirectional Transformer","date":"2020-05-17","arxiv_id":"2005.08294","n_code_links":0,"syntology":null},{"paper":"/paper/tabert-pretraining-for-joint-understanding-of","slug":"tabert-pretraining-for-joint-understanding-of","title":"TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data","date":"2020-05-17","arxiv_id":"2005.08314","n_code_links":1,"syntology":null},{"paper":null,"slug":"cert-contrastive-self-supervised-learning-for","title":"CERT: Contrastive Self-supervised Learning for Language Understanding","date":"2020-05-16","arxiv_id":"2005.12766","n_code_links":0,"syntology":null},{"paper":null,"slug":"leveraging-affective-bidirectional-1","title":"Leveraging Affective Bidirectional Transformers for Offensive Language Detection","date":"2020-05-16","arxiv_id":"2006.01266","n_code_links":0,"syntology":null},{"paper":"/paper/challenges-in-emotion-style-transfer-an","slug":"challenges-in-emotion-style-transfer-an","title":"Challenges in Emotion Style Transfer: An Exploration with a Lexical Substitution Pipeline","date":"2020-05-15","arxiv_id":"2005.07617","n_code_links":1,"syntology":null},{"paper":null,"slug":"cross-lingual-transfer-of-twitter-sentiment","title":"Cross-lingual Transfer of Sentiment Classifiers","date":"2020-05-15","arxiv_id":"2005.07456","n_code_links":0,"syntology":null},{"paper":null,"slug":"keis-just-at-semeval-2020-task-12-identifying","title":"KEIS@JUST at SemEval-2020 Task 12: Identifying Multilingual Offensive Tweets Using Weighted Ensemble and Fine-Tuned BERT","date":"2020-05-15","arxiv_id":"2005.07820","n_code_links":0,"syntology":null},{"paper":null,"slug":"neural-entity-linking-on-technical-service","title":"Neural Entity Linking on Technical Service Tickets","date":"2020-05-15","arxiv_id":"2005.07604","n_code_links":0,"syntology":null},{"paper":"/paper/spelling-error-correction-with-soft-masked","slug":"spelling-error-correction-with-soft-masked","title":"Spelling Error Correction with Soft-Masked BERT","date":"2020-05-15","arxiv_id":"2005.07421","n_code_links":5,"syntology":{"ran":14,"of":17,"n_ran_checked":13,"n_instrument":1,"unverified":3,"pointer_only":3,"phrase":"14 ran (of which 2 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","official":null}},{"paper":"/paper/a-pre-training-technique-to-localize-medical","slug":"a-pre-training-technique-to-localize-medical","title":"Pre-training technique to localize medical BERT and enhance biomedical BERT","date":"2020-05-14","arxiv_id":"2005.07202","n_code_links":1,"syntology":null},{"paper":null,"slug":"nit-agartala-nlp-team-at-semeval-2020-task-8","title":"NIT-Agartala-NLP-Team at SemEval-2020 Task 8: Building Multimodal Classifiers to tackle Internet Humor","date":"2020-05-14","arxiv_id":"2005.06943","n_code_links":0,"syntology":null},{"paper":"/paper/entity-enriched-neural-models-for-clinical","slug":"entity-enriched-neural-models-for-clinical","title":"Entity-Enriched Neural Models for Clinical Question Answering","date":"2020-05-13","arxiv_id":"2005.06587","n_code_links":2,"syntology":null},{"paper":null,"slug":"large-scale-multi-actor-generative-dialog","title":"Large Scale Multi-Actor Generative Dialog Modeling","date":"2020-05-13","arxiv_id":"2005.06114","n_code_links":0,"syntology":null},{"paper":"/paper/parallel-corpus-filtering-via-pre-trained","slug":"parallel-corpus-filtering-via-pre-trained","title":"Parallel Corpus Filtering via Pre-trained Language Models","date":"2020-05-13","arxiv_id":"2005.06166","n_code_links":0,"syntology":null},{"paper":"/paper/on-the-robustness-of-language-encoders","slug":"on-the-robustness-of-language-encoders","title":"On the Robustness of Language Encoders against Grammatical Errors","date":"2020-05-12","arxiv_id":"2005.05683","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"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","official":{"repos":["uclanlp/ProbeGrammarRobustness"],"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"]}}},{"paper":"/paper/skep-sentiment-knowledge-enhanced-pre","slug":"skep-sentiment-knowledge-enhanced-pre","title":"SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis","date":"2020-05-12","arxiv_id":"2005.05635","n_code_links":7,"syntology":null},{"paper":null,"slug":"detecting-adverse-drug-reactions-from-twitter","title":"Detecting Adverse Drug Reactions from Twitter through Domain-Specific Preprocessing and BERT Ensembling","date":"2020-05-11","arxiv_id":"2005.06634","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-generation-of-medical-dialogues-for","title":"On the Generation of Medical Dialogues for COVID-19","date":"2020-05-11","arxiv_id":"2005.05442","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-fpga-based-on-device-reinforcement","title":"An FPGA-Based On-Device Reinforcement Learning Approach using Online Sequential Learning","date":"2020-05-10","arxiv_id":"2005.04646","n_code_links":0,"syntology":null},{"paper":null,"slug":"how-context-affects-language-models-factual","title":"How Context Affects Language Models' Factual Predictions","date":"2020-05-10","arxiv_id":"2005.04611","n_code_links":0,"syntology":null},{"paper":null,"slug":"transformer-based-language-models-for-similar","title":"Transformer Based Language Models for Similar Text Retrieval and Ranking","date":"2020-05-10","arxiv_id":"2005.04588","n_code_links":0,"syntology":null},{"paper":"/paper/finding-universal-grammatical-relations-in","slug":"finding-universal-grammatical-relations-in","title":"Finding Universal Grammatical Relations in Multilingual BERT","date":"2020-05-09","arxiv_id":"2005.04511","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":2,"phrase":"2 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; 0 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","official":{"repos":["ethanachi/multilingual-probing-visualization"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/it-s-morphin-time-combating-linguistic","slug":"it-s-morphin-time-combating-linguistic","title":"It's Morphin' Time! Combating Linguistic Discrimination with Inflectional Perturbations","date":"2020-05-09","arxiv_id":"2005.04364","n_code_links":1,"syntology":null},{"paper":"/paper/lince-a-centralized-benchmark-for-linguistic","slug":"lince-a-centralized-benchmark-for-linguistic","title":"LinCE: A Centralized Benchmark for Linguistic Code-switching Evaluation","date":"2020-05-09","arxiv_id":"2005.04322","n_code_links":0,"syntology":null},{"paper":null,"slug":"schubert-optimizing-elements-of-bert","title":"schuBERT: Optimizing Elements of BERT","date":"2020-05-09","arxiv_id":"2005.06628","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparative-analysis-of-text-classification","title":"Comparative Analysis of Text Classification Approaches in Electronic Health Records","date":"2020-05-08","arxiv_id":"2005.06624","n_code_links":0,"syntology":null},{"paper":null,"slug":"distilling-knowledge-from-pre-trained","title":"Distilling Knowledge from Pre-trained Language Models via Text Smoothing","date":"2020-05-08","arxiv_id":"2005.03848","n_code_links":0,"syntology":null},{"paper":null,"slug":"gobo-quantizing-attention-based-nlp-models","title":"GOBO: Quantizing Attention-Based NLP Models for Low Latency and Energy Efficient Inference","date":"2020-05-08","arxiv_id":"2005.03842","n_code_links":0,"syntology":null},{"paper":"/paper/sentibert-a-transferable-transformer-based","slug":"sentibert-a-transferable-transformer-based","title":"SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics","date":"2020-05-08","arxiv_id":"2005.04114","n_code_links":2,"syntology":null},{"paper":null,"slug":"temporal-common-sense-acquisition-with","title":"Temporal Common Sense Acquisition with Minimal Supervision","date":"2020-05-08","arxiv_id":"2005.04304","n_code_links":0,"syntology":null},{"paper":null,"slug":"liir-at-semeval-2020-task-12-a-cross-lingual","title":"LIIR at SemEval-2020 Task 12: A Cross-Lingual Augmentation Approach for Multilingual Offensive Language Identification","date":"2020-05-07","arxiv_id":"2005.03695","n_code_links":0,"syntology":null},{"paper":"/paper/an-empirical-study-of-multi-task-learning-on","slug":"an-empirical-study-of-multi-task-learning-on","title":"An Empirical Study of Multi-Task Learning on BERT for Biomedical Text Mining","date":"2020-05-06","arxiv_id":"2005.02799","n_code_links":1,"syntology":null},{"paper":"/paper/autoencoding-pixies-amortised-variational","slug":"autoencoding-pixies-amortised-variational","title":"Autoencoding Pixies: Amortised Variational Inference with Graph Convolutions for Functional Distributional Semantics","date":"2020-05-06","arxiv_id":"2005.02991","n_code_links":1,"syntology":null},{"paper":null,"slug":"categorical-vector-space-semantics-for-lambek","title":"Categorical Vector Space Semantics for Lambek Calculus with a Relevant Modality","date":"2020-05-06","arxiv_id":"2005.03074","n_code_links":0,"syntology":null},{"paper":"/paper/harvesting-and-refining-question-answer-pairs","slug":"harvesting-and-refining-question-answer-pairs","title":"Harvesting and Refining Question-Answer Pairs for Unsupervised QA","date":"2020-05-06","arxiv_id":"2005.02925","n_code_links":1,"syntology":null},{"paper":"/paper/contextualizing-hate-speech-classifiers-with","slug":"contextualizing-hate-speech-classifiers-with","title":"Contextualizing Hate Speech Classifiers with Post-hoc Explanation","date":"2020-05-05","arxiv_id":"2005.02439","n_code_links":3,"syntology":{"ran":10,"of":18,"n_ran_checked":6,"n_instrument":4,"unverified":8,"pointer_only":2,"phrase":"10 ran (of which 2 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 4 where Syntology's instrument failed) · 8 unverified","official":{"repos":["BrendanKennedy/contextualizing-hate-speech-models-with-explanations"],"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":["listed","official"]}}},{"paper":"/paper/establishing-baselines-for-text","slug":"establishing-baselines-for-text","title":"Establishing Baselines for Text Classification in Low-Resource Languages","date":"2020-05-05","arxiv_id":"2005.02068","n_code_links":1,"syntology":null},{"paper":"/paper/expbert-representation-engineering-with","slug":"expbert-representation-engineering-with","title":"ExpBERT: Representation Engineering with Natural Language Explanations","date":"2020-05-05","arxiv_id":"2005.01932","n_code_links":2,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"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","official":{"repos":["MurtyShikhar/ExpBERT","worksheets.codalab.org/worksheets/0x609d2d6a66194592a7f44fbb67ba9f49"],"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"]}}},{"paper":"/paper/impactcite-an-xlnet-based-method-for-citation","slug":"impactcite-an-xlnet-based-method-for-citation","title":"ImpactCite: An XLNet-based method for Citation Impact Analysis","date":"2020-05-05","arxiv_id":"2005.06611","n_code_links":1,"syntology":null},{"paper":"/paper/multireqa-a-cross-domain-evaluation-for","slug":"multireqa-a-cross-domain-evaluation-for","title":"MultiReQA: A Cross-Domain Evaluation for Retrieval Question Answering Models","date":"2020-05-05","arxiv_id":"2005.02507","n_code_links":1,"syntology":null},{"paper":"/paper/code-and-named-entity-recognition-in","slug":"code-and-named-entity-recognition-in","title":"Code and Named Entity Recognition in StackOverflow","date":"2020-05-04","arxiv_id":"2005.01634","n_code_links":2,"syntology":{"ran":9,"of":13,"n_ran_checked":7,"n_instrument":2,"unverified":4,"pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 3 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","official":{"repos":["jeniyat/StackOverflowNER"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/distributional-discrepancy-a-metric-for","slug":"distributional-discrepancy-a-metric-for","title":"Distributional Discrepancy: A Metric for Unconditional Text Generation","date":"2020-05-04","arxiv_id":"2005.01282","n_code_links":1,"syntology":null},{"paper":"/paper/robust-encodings-a-framework-for-combating","slug":"robust-encodings-a-framework-for-combating","title":"Robust Encodings: A Framework for Combating Adversarial Typos","date":"2020-05-04","arxiv_id":"2005.01229","n_code_links":1,"syntology":null},{"paper":"/paper/spying-on-your-neighbors-fine-grained-probing","slug":"spying-on-your-neighbors-fine-grained-probing","title":"Spying on your neighbors: Fine-grained probing of contextual embeddings for information about surrounding words","date":"2020-05-04","arxiv_id":"2005.01810","n_code_links":0,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"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","official":null}},{"paper":"/paper/unsupervised-alignment-based-iterative","slug":"unsupervised-alignment-based-iterative","title":"Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering","date":"2020-05-04","arxiv_id":"2005.01218","n_code_links":1,"syntology":null},{"paper":"/paper/encoder-decoder-models-can-benefit-from-pre","slug":"encoder-decoder-models-can-benefit-from-pre","title":"Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction","date":"2020-05-03","arxiv_id":"2005.00987","n_code_links":1,"syntology":null},{"paper":"/paper/transformer-based-end-to-end-question","slug":"transformer-based-end-to-end-question","title":"Simplifying Paragraph-level Question Generation via Transformer Language Models","date":"2020-05-03","arxiv_id":"2005.01107","n_code_links":4,"syntology":{"ran":6,"of":8,"n_ran_checked":6,"n_instrument":0,"unverified":2,"pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":"/paper/a-simple-language-model-for-task-oriented","slug":"a-simple-language-model-for-task-oriented","title":"A Simple Language Model for Task-Oriented Dialogue","date":"2020-05-02","arxiv_id":"2005.00796","n_code_links":1,"syntology":{"ran":13,"of":14,"n_ran_checked":12,"n_instrument":1,"unverified":1,"pointer_only":0,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 1 violated, 11 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["salesforce/simpletod"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/bert-knn-adding-a-knn-search-component-to","slug":"bert-knn-adding-a-knn-search-component-to","title":"BERT-kNN: Adding a kNN Search Component to Pretrained Language Models for Better QA","date":"2020-05-02","arxiv_id":"2005.00766","n_code_links":1,"syntology":null},{"paper":"/paper/birds-have-four-legs-numersense-probing","slug":"birds-have-four-legs-numersense-probing","title":"Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-trained Language Models","date":"2020-05-02","arxiv_id":"2005.00683","n_code_links":0,"syntology":null},{"paper":"/paper/contrastive-self-supervised-learning-for","slug":"contrastive-self-supervised-learning-for","title":"Contrastive Self-Supervised Learning for Commonsense Reasoning","date":"2020-05-02","arxiv_id":"2005.00669","n_code_links":3,"syntology":{"ran":7,"of":8,"n_ran_checked":6,"n_instrument":1,"unverified":1,"pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["SAP-samples/acl2020-commonsense"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/deformer-decomposing-pre-trained-transformers","slug":"deformer-decomposing-pre-trained-transformers","title":"DeFormer: Decomposing Pre-trained Transformers for Faster Question Answering","date":"2020-05-02","arxiv_id":"2005.00697","n_code_links":1,"syntology":{"ran":3,"of":6,"n_ran_checked":1,"n_instrument":2,"unverified":3,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","official":{"repos":["StonyBrookNLP/deformer"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/generating-derivational-morphology-with-bert","slug":"generating-derivational-morphology-with-bert","title":"DagoBERT: Generating Derivational Morphology with a Pretrained Language Model","date":"2020-05-02","arxiv_id":"2005.00672","n_code_links":1,"syntology":null},{"paper":"/paper/isobn-fine-tuning-bert-with-isotropic-batch","slug":"isobn-fine-tuning-bert-with-isotropic-batch","title":"IsoBN: Fine-Tuning BERT with Isotropic Batch Normalization","date":"2020-05-02","arxiv_id":"2005.02178","n_code_links":1,"syntology":null},{"paper":"/paper/a-controllable-model-of-grounded-response","slug":"a-controllable-model-of-grounded-response","title":"A Controllable Model of Grounded Response Generation","date":"2020-05-01","arxiv_id":"2005.00613","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-summarization-dataset-of-slovak-news","title":"A Summarization Dataset of Slovak News Articles","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"abusive-language-in-spanish-children-and","title":"Abusive language in Spanish children and young teenager's conversations: data preparation and short text classification with contextual word embeddings","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"adaptation-of-deep-bidirectional-transformers","title":"Adaptation of Deep Bidirectional Transformers for Afrikaans Language","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"adapting-bert-to-implicit-discourse-relation","title":"Adapting BERT to Implicit Discourse Relation Classification with a Focus on Discourse Connectives","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/aggression-and-misogyny-detection-using-bert","slug":"aggression-and-misogyny-detection-using-bert","title":"Aggression and Misogyny Detection using BERT: A Multi-Task Approach","date":"2020-05-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/aggression-identification-in-english-hindi","slug":"aggression-identification-in-english-hindi","title":"Aggression Identification in English, Hindi and Bangla Text using BERT, RoBERTa and SVM","date":"2020-05-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"aggression-identification-in-social-media-a","title":"Aggression Identification in Social Media: a Transfer Learning Based Approach","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"aia-bde-a-corpus-of-faqs-in-portuguese-and","title":"AIA-BDE: A Corpus of FAQs in Portuguese and their Variations","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"an-evaluation-dataset-for-identifying","title":"An Evaluation Dataset for Identifying Communicative Functions of Sentences in English Scholarly Papers","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"analyzing-elmo-and-distilbert-on-socio","title":"Analyzing ELMo and DistilBERT on Socio-political News Classification","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"automated-essay-scoring-system-for-nonnative","title":"Automated Essay Scoring System for Nonnative Japanese Learners","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/bagging-bert-models-for-robust-aggression","slug":"bagging-bert-models-for-robust-aggression","title":"Bagging BERT Models for Robust Aggression Identification","date":"2020-05-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"building-a-task-oriented-dialog-system-for","title":"Building a Task-oriented Dialog System for Languages with no Training Data: the Case for Basque","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/clinical-reading-comprehension-a-thorough","slug":"clinical-reading-comprehension-a-thorough","title":"Clinical Reading Comprehension: A Thorough Analysis of the emrQA Dataset","date":"2020-05-01","arxiv_id":"2005.00574","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":0,"n_instrument":4,"unverified":0,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","official":{"repos":["xiangyue9607/CliniRC"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/contextualized-embeddings-based-transformer","slug":"contextualized-embeddings-based-transformer","title":"Contextualized Embeddings based Transformer Encoder for Sentence Similarity Modeling in Answer Selection Task","date":"2020-05-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"cross-lingual-and-cross-domain-evaluation-of","title":"Cross-lingual and Cross-domain Evaluation of Machine Reading Comprehension with Squad and CALOR-Quest Corpora","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"cross-lingual-zero-pronoun-resolution","title":"Cross-lingual Zero Pronoun Resolution","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/cross-linguistic-syntactic-evaluation-of-word","slug":"cross-linguistic-syntactic-evaluation-of-word","title":"Cross-Linguistic Syntactic Evaluation of Word Prediction Models","date":"2020-05-01","arxiv_id":"2005.00187","n_code_links":2,"syntology":null},{"paper":"/paper/dane-a-named-entity-resource-for-danish","slug":"dane-a-named-entity-resource-for-danish","title":"DaNE: A Named Entity Resource for Danish","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"development-and-validation-of-a-corpus-for","title":"Development and Validation of a Corpus for Machine Humor Comprehension","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluation-metrics-for-headline-generation","title":"Evaluation Metrics for Headline Generation Using Deep Pre-Trained Embeddings","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"framenet-annotations-alignment-using","title":"FrameNet Annotations Alignment using Attention-based Machine Translation","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"from-web-crawl-to-clean-register-annotated","title":"From Web Crawl to Clean Register-Annotated Corpora","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/hiporank-incorporating-hierarchical-and","slug":"hiporank-incorporating-hierarchical-and","title":"Discourse-Aware Unsupervised Summarization of Long Scientific Documents","date":"2020-05-01","arxiv_id":"2005.00513","n_code_links":1,"syntology":null},{"paper":null,"slug":"hitachi-at-semeval-2020-task-12-offensive","title":"Hitachi at SemEval-2020 Task 12: Offensive Language Identification with Noisy Labels using Statistical Sampling and Post-Processing","date":"2020-05-01","arxiv_id":"2005.00295","n_code_links":0,"syntology":null},{"paper":"/paper/identifying-necessary-elements-for-bert-s","slug":"identifying-necessary-elements-for-bert-s","title":"Identifying Necessary Elements for BERT's Multilinguality","date":"2020-05-01","arxiv_id":"2005.00396","n_code_links":1,"syntology":null},{"paper":null,"slug":"implementation-of-supervised-training","title":"Implementation of Supervised Training Approaches for Monolingual Word Sense Alignment: ACDH-CH System Description for the MWSA Shared Task at GlobaLex 2020","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-neural-language-generation-with","title":"Improving Neural Language Generation with Spectrum Control","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/information-seeking-in-the-spirit-of-learning","slug":"information-seeking-in-the-spirit-of-learning","title":"Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity","date":"2020-05-01","arxiv_id":"2005.00172","n_code_links":1,"syntology":null},{"paper":null,"slug":"intermediate-task-transfer-learning-with","title":"Intermediate-Task Transfer Learning with Pretrained Models for Natural Language Understanding: When and Why Does It Work?","date":"2020-05-01","arxiv_id":"2005.00628","n_code_links":0,"syntology":null},{"paper":null,"slug":"introducing-a-large-scale-dataset-for","title":"Introducing a Large-Scale Dataset for Vietnamese POS Tagging on Conversational Texts","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"irit-at-trac-2020","title":"IRIT at TRAC 2020","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"is-language-modeling-enough-evaluating","title":"Is Language Modeling Enough? Evaluating Effective Embedding Combinations","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/joint-learning-of-syntactic-features-helps","slug":"joint-learning-of-syntactic-features-helps","title":"Joint Learning of Syntactic Features Helps Discourse Segmentation","date":"2020-05-01","arxiv_id":null,"n_code_links":1,"syntology":null}],"record_sha256":"d3f627b3eadab383d28da73887e50ab38345baa5d29cbb1307343b5d25a699b4","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}