{"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/attention/papers/301","list_of":"/method/attention","method":"Attention","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":301,"pages_in_order":316,"rows_per_page":100,"rows":[30001,30100],"of":31583,"counts":{"archive_papers_tagged":31583,"with_a_code_link":13473,"where_syntology_ran_a_sample":3998,"not_listed_spam_title":0,"listed":31583,"listed_where_code_ran":3998,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":3366,"every_run_a_failure_of_syntologys_instrument":632,"listed_with_a_run_with_no_instrument_failure":3366,"listed_every_run_a_failure_of_syntologys_instrument":632,"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/attention","prev":"/method/attention/papers/300","next":"/method/attention/papers/302","papers":[{"paper":"/paper/transformer-reasoning-network-for-image-text","slug":"transformer-reasoning-network-for-image-text","title":"Transformer Reasoning Network for Image-Text Matching and Retrieval","date":"2020-04-20","arxiv_id":"2004.09144","n_code_links":1,"syntology":{"ran":12,"of":14,"n_ran_checked":11,"n_instrument":1,"unverified":2,"pointer_only":2,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 1 violated, 10 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["mesnico/TERN"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"whaletrans-e2e-whisper-to-natural-speech","title":"End-to-End Whisper to Natural Speech Conversion using Modified Transformer Network","date":"2020-04-20","arxiv_id":"2004.09347","n_code_links":0,"syntology":null},{"paper":"/paper/enhancing-pharmacovigilance-with-drug-reviews","slug":"enhancing-pharmacovigilance-with-drug-reviews","title":"Enhancing Pharmacovigilance with Drug Reviews and Social Media","date":"2020-04-18","arxiv_id":"2004.08731","n_code_links":1,"syntology":null},{"paper":"/paper/motion-segmentation-using-frequency-domain","slug":"motion-segmentation-using-frequency-domain","title":"Motion Segmentation using Frequency Domain Transformer Networks","date":"2020-04-18","arxiv_id":"2004.08638","n_code_links":1,"syntology":null},{"paper":null,"slug":"enriching-the-transformer-with-linguistic-and","title":"Enriching the Transformer with Linguistic Factors for Low-Resource Machine Translation","date":"2020-04-17","arxiv_id":"2004.08053","n_code_links":0,"syntology":null},{"paper":"/paper/etc-encoding-long-and-structured-data-in","slug":"etc-encoding-long-and-structured-data-in","title":"ETC: Encoding Long and Structured Inputs in Transformers","date":"2020-04-17","arxiv_id":"2004.08483","n_code_links":2,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"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","official":{"repos":["google-research/google-research"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/fast-and-accurate-deep-bidirectional-language","slug":"fast-and-accurate-deep-bidirectional-language","title":"Fast and Accurate Deep Bidirectional Language Representations for Unsupervised Learning","date":"2020-04-17","arxiv_id":"2004.08097","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":{"repos":["joongbo/tta"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":"/paper/highway-transformer-self-gating-enhanced-self","slug":"highway-transformer-self-gating-enhanced-self","title":"Highway Transformer: Self-Gating Enhanced Self-Attentive Networks","date":"2020-04-17","arxiv_id":"2004.08178","n_code_links":1,"syntology":{"ran":4,"of":9,"n_ran_checked":4,"n_instrument":0,"unverified":5,"pointer_only":0,"phrase":"4 ran (of which 4 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) · 5 unverified; every one of the 4 samples that ran constructed an object rather than computing a result","official":{"repos":["cyk1337/Highway-Transformer"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"learning-to-rank-with-bert-in-tf-ranking","title":"Learning-to-Rank with BERT in TF-Ranking","date":"2020-04-17","arxiv_id":"2004.08476","n_code_links":0,"syntology":null},{"paper":null,"slug":"too-many-claims-to-fact-check-prioritizing","title":"Too Many Claims to Fact-Check: Prioritizing Political Claims Based on Check-Worthiness","date":"2020-04-17","arxiv_id":"2004.08166","n_code_links":0,"syntology":null},{"paper":"/paper/transform-and-tell-entity-aware-news-image","slug":"transform-and-tell-entity-aware-news-image","title":"Transform and Tell: Entity-Aware News Image Captioning","date":"2020-04-17","arxiv_id":"2004.08070","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":2,"phrase":"2 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; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["alasdairtran/transform-and-tell"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/understanding-the-difficulty-of-training","slug":"understanding-the-difficulty-of-training","title":"Understanding the Difficulty of Training Transformers","date":"2020-04-17","arxiv_id":"2004.08249","n_code_links":2,"syntology":{"ran":4,"of":5,"n_ran_checked":3,"n_instrument":1,"unverified":1,"pointer_only":0,"phrase":"4 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["LiyuanLucasLiu/Transforemr-Clinic"],"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":["listed","official"]}}},{"paper":"/paper/cross-lingual-contextualized-topic-models","slug":"cross-lingual-contextualized-topic-models","title":"Cross-lingual Contextualized Topic Models with Zero-shot Learning","date":"2020-04-16","arxiv_id":"2004.07737","n_code_links":2,"syntology":{"ran":6,"of":7,"n_ran_checked":6,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["MilaNLProc/contextualized-topic-models"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/non-autoregressive-machine-translation-with-1","slug":"non-autoregressive-machine-translation-with-1","title":"Non-Autoregressive Machine Translation with Latent Alignments","date":"2020-04-16","arxiv_id":"2004.07437","n_code_links":2,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"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) · 1 unverified","official":null}},{"paper":"/paper/the-right-tool-for-the-job-matching-model-and","slug":"the-right-tool-for-the-job-matching-model-and","title":"The Right Tool for the Job: Matching Model and Instance Complexities","date":"2020-04-16","arxiv_id":"2004.07453","n_code_links":1,"syntology":{"ran":5,"of":8,"n_ran_checked":2,"n_instrument":3,"unverified":3,"pointer_only":2,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","official":{"repos":["allenai/sledgehammer"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"towards-instance-level-parser-selection-for","title":"Towards Instance-Level Parser Selection for Cross-Lingual Transfer of Dependency Parsers","date":"2020-04-16","arxiv_id":"2004.07642","n_code_links":0,"syntology":null},{"paper":"/paper/coreferential-reasoning-learning-for-language","slug":"coreferential-reasoning-learning-for-language","title":"Coreferential Reasoning Learning for Language Representation","date":"2020-04-15","arxiv_id":"2004.06870","n_code_links":2,"syntology":null},{"paper":"/paper/document-level-representation-learning-using","slug":"document-level-representation-learning-using","title":"SPECTER: Document-level Representation Learning using Citation-informed Transformers","date":"2020-04-15","arxiv_id":"2004.07180","n_code_links":5,"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":["allenai/scidocs","allenai/specter"],"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/entities-as-experts-sparse-memory-access-with","slug":"entities-as-experts-sparse-memory-access-with","title":"Entities as Experts: Sparse Memory Access with Entity Supervision","date":"2020-04-15","arxiv_id":"2004.07202","n_code_links":1,"syntology":{"ran":11,"of":14,"n_ran_checked":11,"n_instrument":0,"unverified":3,"pointer_only":14,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":null}},{"paper":null,"slug":"lambert-language-and-action-learning-using","title":"lamBERT: Language and Action Learning Using Multimodal BERT","date":"2020-04-15","arxiv_id":"2004.07093","n_code_links":0,"syntology":null},{"paper":"/paper/sentiment-analysis-of-yelp-reviews-a","slug":"sentiment-analysis-of-yelp-reviews-a","title":"Sentiment Analysis of Yelp Reviews: A Comparison of Techniques and Models","date":"2020-04-15","arxiv_id":"2004.13851","n_code_links":1,"syntology":null},{"paper":"/paper/tod-bert-pre-trained-natural-language","slug":"tod-bert-pre-trained-natural-language","title":"TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue","date":"2020-04-15","arxiv_id":"2004.06871","n_code_links":1,"syntology":{"ran":4,"of":13,"n_ran_checked":4,"n_instrument":0,"unverified":9,"pointer_only":13,"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) · 9 unverified","official":{"repos":["jasonwu0731/ToD-BERT"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":9,"ran_from_kinds":["official"]}}},{"paper":"/paper/training-with-quantization-noise-for-extreme","slug":"training-with-quantization-noise-for-extreme","title":"Training with Quantization Noise for Extreme Model Compression","date":"2020-04-15","arxiv_id":"2004.07320","n_code_links":4,"syntology":null},{"paper":"/paper/a-simple-yet-strong-pipeline-for-hotpotqa","slug":"a-simple-yet-strong-pipeline-for-hotpotqa","title":"A Simple Yet Strong Pipeline for HotpotQA","date":"2020-04-14","arxiv_id":"2004.06753","n_code_links":0,"syntology":null},{"paper":"/paper/deep-learning-models-for-multilingual-hate","slug":"deep-learning-models-for-multilingual-hate","title":"Deep Learning Models for Multilingual Hate Speech Detection","date":"2020-04-14","arxiv_id":"2004.06465","n_code_links":3,"syntology":null},{"paper":"/paper/palm-pre-training-an-autoencoding","slug":"palm-pre-training-an-autoencoding","title":"PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation","date":"2020-04-14","arxiv_id":"2004.07159","n_code_links":2,"syntology":null},{"paper":null,"slug":"standardizing-and-benchmarking-crisis-related","title":"CrisisBench: Benchmarking Crisis-related Social Media Datasets for Humanitarian Information Processing","date":"2020-04-14","arxiv_id":"2004.06774","n_code_links":0,"syntology":null},{"paper":"/paper/transformer-based-grapheme-to-phoneme-1","slug":"transformer-based-grapheme-to-phoneme-1","title":"Transformer based Grapheme-to-Phoneme Conversion","date":"2020-04-14","arxiv_id":"2004.06338","n_code_links":1,"syntology":null},{"paper":"/paper/what-s-so-special-about-bert-s-layers-a","slug":"what-s-so-special-about-bert-s-layers-a","title":"What's so special about BERT's layers? A closer look at the NLP pipeline in monolingual and multilingual models","date":"2020-04-14","arxiv_id":"2004.06499","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"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","official":{"repos":["wietsedv/bertje"],"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"]}}},{"paper":null,"slug":"cascade-neural-ensemble-for-identifying","title":"Cascade Neural Ensemble for Identifying Scientifically Sound Articles","date":"2020-04-13","arxiv_id":"2004.06222","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-scholarly-knowledge-representation","title":"Improving Scholarly Knowledge Representation: Evaluating BERT-based Models for Scientific Relation Classification","date":"2020-04-13","arxiv_id":"2004.06153","n_code_links":0,"syntology":null},{"paper":"/paper/pretrained-transformers-improve-out-of","slug":"pretrained-transformers-improve-out-of","title":"Pretrained Transformers Improve Out-of-Distribution Robustness","date":"2020-04-13","arxiv_id":"2004.06100","n_code_links":1,"syntology":null},{"paper":null,"slug":"proformer-towards-on-device-lsh-projection","title":"ProFormer: Towards On-Device LSH Projection Based Transformers","date":"2020-04-13","arxiv_id":"2004.05801","n_code_links":0,"syntology":null},{"paper":"/paper/public-self-consciousness-for-endowing","slug":"public-self-consciousness-for-endowing","title":"Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness","date":"2020-04-13","arxiv_id":"2004.05816","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["skywalker023/pragmatic-consistency"],"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"]}}},{"paper":"/paper/relation-transformer-network","slug":"relation-transformer-network","title":"Relation Transformer Network","date":"2020-04-13","arxiv_id":"2004.06193","n_code_links":1,"syntology":null},{"paper":null,"slug":"robustly-pre-trained-neural-model-for-direct","title":"Robustly Pre-trained Neural Model for Direct Temporal Relation Extraction","date":"2020-04-13","arxiv_id":"2004.06216","n_code_links":0,"syntology":null},{"paper":null,"slug":"unified-multi-criteria-chinese-word","title":"Unified Multi-Criteria Chinese Word Segmentation with BERT","date":"2020-04-13","arxiv_id":"2004.05808","n_code_links":0,"syntology":null},{"paper":"/paper/amr-parsing-via-graph-sequence-iterative","slug":"amr-parsing-via-graph-sequence-iterative","title":"AMR Parsing via Graph-Sequence Iterative Inference","date":"2020-04-12","arxiv_id":"2004.05572","n_code_links":3,"syntology":{"ran":21,"of":31,"n_ran_checked":16,"n_instrument":5,"unverified":10,"pointer_only":1,"phrase":"21 ran (of which 11 constructed an object rather than computing a result; 16 with no instrument failure: 1 honoured, 1 violated, 14 with no contract checked; 5 where Syntology's instrument failed) · 10 unverified","official":{"repos":["jcyk/AMR-gs"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["listed","official","unlocated"]}}},{"paper":null,"slug":"pre-training-text-representations-as-meta","title":"Pre-training Text Representations as Meta Learning","date":"2020-04-12","arxiv_id":"2004.05568","n_code_links":0,"syntology":null},{"paper":null,"slug":"relational-learning-between-multiple","title":"Relational Learning between Multiple Pulmonary Nodules via Deep Set Attention Transformers","date":"2020-04-12","arxiv_id":"2004.05640","n_code_links":0,"syntology":null},{"paper":"/paper/vgcn-bert-augmenting-bert-with-graph","slug":"vgcn-bert-augmenting-bert-with-graph","title":"VGCN-BERT: Augmenting BERT with Graph Embedding for Text Classification","date":"2020-04-12","arxiv_id":"2004.05707","n_code_links":1,"syntology":{"ran":7,"of":9,"n_ran_checked":5,"n_instrument":2,"unverified":2,"pointer_only":2,"phrase":"7 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; 2 where Syntology's instrument failed) · 2 unverified","official":{"repos":["Louis-udm/VGCN-BERT"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"end-to-end-chinese-lexical-fusion-recognition","title":"End to End Chinese Lexical Fusion Recognition with Sememe Knowledge","date":"2020-04-11","arxiv_id":"2004.05456","n_code_links":0,"syntology":null},{"paper":"/paper/lareqa-language-agnostic-answer-retrieval","slug":"lareqa-language-agnostic-answer-retrieval","title":"LAReQA: Language-agnostic answer retrieval from a multilingual pool","date":"2020-04-11","arxiv_id":"2004.05484","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-in-depth-walkthrough-on-evolution-of","title":"An In-depth Walkthrough on Evolution of Neural Machine Translation","date":"2020-04-10","arxiv_id":"2004.04902","n_code_links":0,"syntology":null},{"paper":"/paper/longformer-the-long-document-transformer","slug":"longformer-the-long-document-transformer","title":"Longformer: The Long-Document Transformer","date":"2020-04-10","arxiv_id":"2004.05150","n_code_links":22,"syntology":{"ran":22,"of":35,"n_ran_checked":14,"n_instrument":8,"unverified":13,"pointer_only":5,"phrase":"22 ran (of which 4 constructed an object rather than computing a result; 14 with no instrument failure: 1 honoured, 1 violated, 12 with no contract checked; 8 where Syntology's instrument failed) · 13 unverified","official":{"repos":["allenai/longformer"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"simpletran-transferring-pre-trained-sentence","title":"Beyond Fine-tuning: Few-Sample Sentence Embedding Transfer","date":"2020-04-10","arxiv_id":"2004.05119","n_code_links":0,"syntology":null},{"paper":null,"slug":"stacked-convolutional-deep-encoding-network","title":"Stacked Convolutional Deep Encoding Network for Video-Text Retrieval","date":"2020-04-10","arxiv_id":"2004.04959","n_code_links":0,"syntology":null},{"paper":null,"slug":"telling-bert-s-full-story-from-local","title":"Telling BERT's full story: from Local Attention to Global Aggregation","date":"2020-04-10","arxiv_id":"2004.05916","n_code_links":0,"syntology":null},{"paper":"/paper/bleurt-learning-robust-metrics-for-text","slug":"bleurt-learning-robust-metrics-for-text","title":"BLEURT: Learning Robust Metrics for Text Generation","date":"2020-04-09","arxiv_id":"2004.04696","n_code_links":4,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"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) · 1 unverified","official":{"repos":["google-research/bleurt"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/cortical-surface-registration-using","slug":"cortical-surface-registration-using","title":"Cortical surface registration using unsupervised learning","date":"2020-04-09","arxiv_id":"2004.04617","n_code_links":1,"syntology":null},{"paper":null,"slug":"interpretability-analysis-for-named-entity","title":"Interpretability Analysis for Named Entity Recognition to Understand System Predictions and How They Can Improve","date":"2020-04-09","arxiv_id":"2004.04564","n_code_links":0,"syntology":null},{"paper":"/paper/on-optimal-transformer-depth-for-low-resource","slug":"on-optimal-transformer-depth-for-low-resource","title":"On Optimal Transformer Depth for Low-Resource Language Translation","date":"2020-04-09","arxiv_id":"2004.04418","n_code_links":1,"syntology":null},{"paper":"/paper/on-the-language-neutrality-of-pre-trained","slug":"on-the-language-neutrality-of-pre-trained","title":"On the Language Neutrality of Pre-trained Multilingual Representations","date":"2020-04-09","arxiv_id":"2004.05160","n_code_links":1,"syntology":{"ran":4,"of":6,"n_ran_checked":1,"n_instrument":3,"unverified":2,"pointer_only":0,"phrase":"4 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; 3 where Syntology's instrument failed) · 2 unverified","official":{"repos":["jlibovicky/assess-multilingual-bert"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"sequential-neural-rendering-with-transformer","title":"Sequential View Synthesis with Transformer","date":"2020-04-09","arxiv_id":"2004.04548","n_code_links":0,"syntology":null},{"paper":"/paper/adaptive-transformers-in-rl","slug":"adaptive-transformers-in-rl","title":"Adaptive Transformers in RL","date":"2020-04-08","arxiv_id":"2004.03761","n_code_links":1,"syntology":null},{"paper":"/paper/dialbert-a-hierarchical-pre-trained-model-for","slug":"dialbert-a-hierarchical-pre-trained-model-for","title":"DialBERT: A Hierarchical Pre-Trained Model for Conversation Disentanglement","date":"2020-04-08","arxiv_id":"2004.03760","n_code_links":1,"syntology":null},{"paper":"/paper/diverse-controllable-and-keyphrase-aware-a","slug":"diverse-controllable-and-keyphrase-aware-a","title":"Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline Generation","date":"2020-04-08","arxiv_id":"2004.03875","n_code_links":1,"syntology":null},{"paper":"/paper/dynabert-dynamic-bert-with-adaptive-width-and","slug":"dynabert-dynamic-bert-with-adaptive-width-and","title":"DynaBERT: Dynamic BERT with Adaptive Width and Depth","date":"2020-04-08","arxiv_id":"2004.04037","n_code_links":3,"syntology":{"ran":5,"of":8,"n_ran_checked":2,"n_instrument":3,"unverified":3,"pointer_only":8,"phrase":"5 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","official":{"repos":["huawei-noah/Pretrained-Language-Model"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"error-correction-and-extraction-in-request","title":"Error correction and extraction in request dialogs","date":"2020-04-08","arxiv_id":"2004.04243","n_code_links":0,"syntology":null},{"paper":"/paper/exploiting-redundancy-in-pre-trained-language","slug":"exploiting-redundancy-in-pre-trained-language","title":"Analyzing Redundancy in Pretrained Transformer Models","date":"2020-04-08","arxiv_id":"2004.04010","n_code_links":1,"syntology":null},{"paper":null,"slug":"generating-counter-narratives-against-online","title":"Generating Counter Narratives against Online Hate Speech: Data and Strategies","date":"2020-04-08","arxiv_id":"2004.04216","n_code_links":0,"syntology":null},{"paper":"/paper/have-your-text-and-use-it-too-end-to-end","slug":"have-your-text-and-use-it-too-end-to-end","title":"Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity","date":"2020-04-08","arxiv_id":"2004.06577","n_code_links":1,"syntology":null},{"paper":"/paper/improving-bert-with-self-supervised-attention","slug":"improving-bert-with-self-supervised-attention","title":"Improving BERT with Self-Supervised Attention","date":"2020-04-08","arxiv_id":"2004.03808","n_code_links":1,"syntology":null},{"paper":null,"slug":"ladabert-lightweight-adaptation-of-bert","title":"LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression","date":"2020-04-08","arxiv_id":"2004.04124","n_code_links":0,"syntology":null},{"paper":"/paper/poor-man-s-bert-smaller-and-faster","slug":"poor-man-s-bert-smaller-and-faster","title":"On the Effect of Dropping Layers of Pre-trained Transformer Models","date":"2020-04-08","arxiv_id":"2004.03844","n_code_links":4,"syntology":{"ran":7,"of":8,"n_ran_checked":4,"n_instrument":3,"unverified":1,"pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","official":{"repos":["hsajjad/transformers"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/pre-training-is-a-hot-topic-contextualized","slug":"pre-training-is-a-hot-topic-contextualized","title":"Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence","date":"2020-04-08","arxiv_id":"2004.03974","n_code_links":3,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"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","official":{"repos":["MilaNLProc/contextualized-topic-models"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/sciwing-a-software-toolkit-for-scientific","slug":"sciwing-a-software-toolkit-for-scientific","title":"SciWING -- A Software Toolkit for Scientific Document Processing","date":"2020-04-08","arxiv_id":"2004.03807","n_code_links":1,"syntology":null},{"paper":null,"slug":"severing-the-edge-between-before-and-after","title":"Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events","date":"2020-04-08","arxiv_id":"2004.04295","n_code_links":0,"syntology":null},{"paper":"/paper/are-natural-language-inference-models","slug":"are-natural-language-inference-models","title":"Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition","date":"2020-04-07","arxiv_id":"2004.03066","n_code_links":1,"syntology":{"ran":2,"of":5,"n_ran_checked":2,"n_instrument":0,"unverified":3,"pointer_only":5,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["alexwarstadt/data_generation"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["found_in_text"]}}},{"paper":"/paper/byte-pair-encoding-is-suboptimal-for-language","slug":"byte-pair-encoding-is-suboptimal-for-language","title":"Byte Pair Encoding is Suboptimal for Language Model Pretraining","date":"2020-04-07","arxiv_id":"2004.03720","n_code_links":1,"syntology":null},{"paper":"/paper/evaluating-machines-by-their-real-world","slug":"evaluating-machines-by-their-real-world","title":"TuringAdvice: A Generative and Dynamic Evaluation of Language Use","date":"2020-04-07","arxiv_id":"2004.03607","n_code_links":1,"syntology":null},{"paper":"/paper/information-theoretic-probing-for-linguistic","slug":"information-theoretic-probing-for-linguistic","title":"Information-Theoretic Probing for Linguistic Structure","date":"2020-04-07","arxiv_id":"2004.03061","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":1,"n_instrument":0,"unverified":1,"pointer_only":0,"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","official":{"repos":["rycolab/info-theoretic-probing"],"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"]}}},{"paper":"/paper/probabilistic-spatial-transformers-for","slug":"probabilistic-spatial-transformers-for","title":"Probabilistic Spatial Transformer Networks","date":"2020-04-07","arxiv_id":"2004.03637","n_code_links":1,"syntology":null},{"paper":"/paper/ryansql-recursively-applying-sketch-based","slug":"ryansql-recursively-applying-sketch-based","title":"RYANSQL: Recursively Applying Sketch-based Slot Fillings for Complex Text-to-SQL in Cross-Domain Databases","date":"2020-04-07","arxiv_id":"2004.03125","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"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) · 1 unverified","official":{"repos":["kakaoenterprise/RYANSQL"],"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"]}}},{"paper":"/paper/speaker-aware-bert-for-multi-turn-response","slug":"speaker-aware-bert-for-multi-turn-response","title":"Speaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based Chatbots","date":"2020-04-07","arxiv_id":"2004.03588","n_code_links":2,"syntology":null},{"paper":null,"slug":"textgail-generative-adversarial-imitation","title":"TextGAIL: Generative Adversarial Imitation Learning for Text Generation","date":"2020-04-07","arxiv_id":"2004.13796","n_code_links":0,"syntology":null},{"paper":"/paper/the-russian-drug-reaction-corpus-and-neural","slug":"the-russian-drug-reaction-corpus-and-neural","title":"The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews","date":"2020-04-07","arxiv_id":"2004.03659","n_code_links":1,"syntology":null},{"paper":null,"slug":"towards-evaluating-the-robustness-of-chinese","title":"Towards Evaluating the Robustness of Chinese BERT Classifiers","date":"2020-04-07","arxiv_id":"2004.03742","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-non-task-specific-distillation-of","title":"Towards Non-task-specific Distillation of BERT via Sentence Representation Approximation","date":"2020-04-07","arxiv_id":"2004.03097","n_code_links":0,"syntology":null},{"paper":"/paper/transformers-to-learn-hierarchical-contexts","slug":"transformers-to-learn-hierarchical-contexts","title":"Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-based Question Answering","date":"2020-04-07","arxiv_id":"2004.03561","n_code_links":1,"syntology":null},{"paper":"/paper/a-systematic-analysis-of-morphological","slug":"a-systematic-analysis-of-morphological","title":"A Systematic Analysis of Morphological Content in BERT Models for Multiple Languages","date":"2020-04-06","arxiv_id":"2004.03032","n_code_links":1,"syntology":null},{"paper":"/paper/autotoon-automatic-geometric-warping-for-face","slug":"autotoon-automatic-geometric-warping-for-face","title":"AutoToon: Automatic Geometric Warping for Face Cartoon Generation","date":"2020-04-06","arxiv_id":"2004.02377","n_code_links":1,"syntology":null},{"paper":"/paper/bootstrapping-a-crosslingual-semantic-parser","slug":"bootstrapping-a-crosslingual-semantic-parser","title":"Bootstrapping a Crosslingual Semantic Parser","date":"2020-04-06","arxiv_id":"2004.02585","n_code_links":1,"syntology":null},{"paper":null,"slug":"dare-data-augmented-relation-extraction-with","title":"DARE: Data Augmented Relation Extraction with GPT-2","date":"2020-04-06","arxiv_id":"2004.13845","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-review-comprehension-with-domain","title":"Enhancing Review Comprehension with Domain-Specific Commonsense","date":"2020-04-06","arxiv_id":"2004.03020","n_code_links":0,"syntology":null},{"paper":null,"slug":"leveraging-the-inherent-hierarchy-of-vacancy","title":"Leveraging the Inherent Hierarchy of Vacancy Titles for Automated Job Ontology Expansion","date":"2020-04-06","arxiv_id":"2004.02814","n_code_links":0,"syntology":null},{"paper":"/paper/mobilebert-a-compact-task-agnostic-bert-for","slug":"mobilebert-a-compact-task-agnostic-bert-for","title":"MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices","date":"2020-04-06","arxiv_id":"2004.02984","n_code_links":7,"syntology":null},{"paper":"/paper/sparse-text-generation","slug":"sparse-text-generation","title":"Sparse Text Generation","date":"2020-04-06","arxiv_id":"2004.02644","n_code_links":1,"syntology":null},{"paper":"/paper/fastbert-a-self-distilling-bert-with-adaptive","slug":"fastbert-a-self-distilling-bert-with-adaptive","title":"FastBERT: a Self-distilling BERT with Adaptive Inference Time","date":"2020-04-05","arxiv_id":"2004.02178","n_code_links":3,"syntology":{"ran":6,"of":11,"n_ran_checked":6,"n_instrument":0,"unverified":5,"pointer_only":2,"phrase":"6 ran (of which 4 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) · 5 unverified","official":{"repos":["autoliuweijie/FastBERT"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"improved-pretraining-for-domain-specific","title":"Continual Domain-Tuning for Pretrained Language Models","date":"2020-04-05","arxiv_id":"2004.02288","n_code_links":0,"syntology":null},{"paper":"/paper/optimus-organizing-sentences-via-pre-trained","slug":"optimus-organizing-sentences-via-pre-trained","title":"Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space","date":"2020-04-05","arxiv_id":"2004.04092","n_code_links":1,"syntology":null},{"paper":null,"slug":"syntax-driven-iterative-expansion-language","title":"Syntax-driven Iterative Expansion Language Models for Controllable Text Generation","date":"2020-04-05","arxiv_id":"2004.02211","n_code_links":0,"syntology":null},{"paper":"/paper/a-dependency-syntactic-knowledge-augmented","slug":"a-dependency-syntactic-knowledge-augmented","title":"A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment Analysis","date":"2020-04-04","arxiv_id":"2004.01951","n_code_links":3,"syntology":null},{"paper":null,"slug":"cg-bert-conditional-text-generation-with-bert","title":"CG-BERT: Conditional Text Generation with BERT for Generalized Few-shot Intent Detection","date":"2020-04-04","arxiv_id":"2004.01881","n_code_links":0,"syntology":null},{"paper":null,"slug":"conversational-question-reformulation-via","title":"Conversational Question Reformulation via Sequence-to-Sequence Architectures and Pretrained Language Models","date":"2020-04-04","arxiv_id":"2004.01909","n_code_links":0,"syntology":null},{"paper":"/paper/end-to-end-abstractive-summarization-for","slug":"end-to-end-abstractive-summarization-for","title":"A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining","date":"2020-04-04","arxiv_id":"2004.02016","n_code_links":3,"syntology":null},{"paper":null,"slug":"generating-rationales-in-visual-question","title":"Generating Rationales in Visual Question Answering","date":"2020-04-04","arxiv_id":"2004.02032","n_code_links":0,"syntology":null},{"paper":null,"slug":"step-sequence-to-sequence-transformer-pre","title":"Pre-training for Abstractive Document Summarization by Reinstating Source Text","date":"2020-04-04","arxiv_id":"2004.01853","n_code_links":0,"syntology":null},{"paper":"/paper/keyphrase-rubric-relationship-classification","slug":"keyphrase-rubric-relationship-classification","title":"Finding Black Cat in a Coal Cellar -- Keyphrase Extraction & Keyphrase-Rubric Relationship Classification from Complex Assignments","date":"2020-04-03","arxiv_id":"2004.01549","n_code_links":2,"syntology":null},{"paper":"/paper/lidar-based-online-3d-video-object-detection","slug":"lidar-based-online-3d-video-object-detection","title":"LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention","date":"2020-04-03","arxiv_id":"2004.01389","n_code_links":1,"syntology":null}],"record_sha256":"e77088983679fd235e88ed60ff9884183c26f973ebcb978141de1c3db357edc3","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}