{"url":"/sota/question-answering-on-squad11","task":{"name":"Question Answering","url":"/task/question-answering","note":null},"dataset":{"name":"SQuAD1.1","url":"/dataset/squad"},"category":"Natural Language Processing","categories":["Miscellaneous","Natural Language Processing","Reasoning"],"category_note":null,"description":"Question answering can be segmented into domain-specific tasks like community question answering and knowledge-base question answering. Popular benchmark datasets for evaluation question answering systems include [SQuAD](/dataset/squad), [HotPotQA](/dataset/hotpotqa), [bAbI](/dataset/babi-1), [TriviaQA](/dataset/triviaqa), [WikiQA](/dataset/wikiqa), and many others. Models for question answering are typically evaluated on metrics like EM and F1. Some recent top performing models are T5 and XLNet.\r\n\r\n( Image credit: [SQuAD](https://rajpurkar.github.io/mlx/qa-and-squad/) )","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["EM","F1","Hardware Burden","Exact Match","Operations per network pass"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"EM":null,"F1":"higher","Hardware Burden":null,"Exact Match":null,"Operations per network pass":null}},"counts":{"rows":213,"rows_with_code":51,"rows_with_paper_page":76,"rows_dated":76,"rows_using_additional_data":6},"rows":[{"rank_in_archive_order":1,"model":"{ANNA} (single model)","metrics":{"EM":"90.622","F1":"95.719"},"uses_additional_data":true,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":2,"model":"LUKE (single model)","metrics":{"EM":"90.202","F1":"95.379"},"uses_additional_data":true,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":3,"model":"LUKE (single model)","metrics":{"EM":"90.202","F1":"95.379"},"uses_additional_data":false,"paper_date":"2020-10-02","paper":"/paper/luke-deep-contextualized-entity","paper_url":"https://arxiv.org/abs/2010.01057v1","paper_title":"LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention","code":"https://github.com/huggingface/transformers","n_code_links":9,"syntology":{"n_ran":3,"n_unverified":7,"n_samples":10,"n_pointer_only_licence":0}},{"rank_in_archive_order":4,"model":"LUKE","metrics":{"EM":"90.2"},"uses_additional_data":false,"paper_date":"2020-10-02","paper":"/paper/luke-deep-contextualized-entity","paper_url":"https://arxiv.org/abs/2010.01057v1","paper_title":"LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention","code":"https://github.com/huggingface/transformers","n_code_links":9,"syntology":{"n_ran":3,"n_unverified":7,"n_samples":10,"n_pointer_only_licence":0}},{"rank_in_archive_order":5,"model":"XLNet (single model)","metrics":{"EM":"89.898","F1":"95.080"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":6,"model":"XLNet (single model)","metrics":{"EM":"89.898","F1":"95.080","Hardware Burden":"46449G"},"uses_additional_data":true,"paper_date":"2019-06-19","paper":"/paper/xlnet-generalized-autoregressive-pretraining","paper_url":"https://arxiv.org/abs/1906.08237v2","paper_title":"XLNet: Generalized Autoregressive Pretraining for Language Understanding","code":"https://github.com/huggingface/transformers","n_code_links":27,"syntology":{"n_ran":10,"n_unverified":14,"n_samples":24,"n_pointer_only_licence":3}},{"rank_in_archive_order":7,"model":"XLNET-123++ (single model)","metrics":{"EM":"89.856","F1":"94.903"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":8,"model":"XLNET-123+ (single model)","metrics":{"EM":"89.709","F1":"94.859"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":9,"model":"XLNET-123 (single model)","metrics":{"EM":"89.646","F1":"94.930"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":10,"model":"Unnamed submission by NMC","metrics":{"EM":"88.912","F1":"94.584"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":11,"model":"BERTSP (single model)","metrics":{"EM":"88.912","F1":"94.584"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":12,"model":"SpanBERT (single model)","metrics":{"EM":"88.839","F1":"94.635"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":13,"model":"SpanBERT (single model)","metrics":{"EM":"88.8","F1":"94.6","Hardware Burden":"586G"},"uses_additional_data":false,"paper_date":"2019-07-24","paper":"/paper/spanbert-improving-pre-training-by","paper_url":"https://arxiv.org/abs/1907.10529v3","paper_title":"SpanBERT: Improving Pre-training by Representing and Predicting Spans","code":"https://github.com/facebookresearch/SpanBERT","n_code_links":6,"syntology":{"n_ran":3,"n_unverified":12,"n_samples":15,"n_pointer_only_licence":4}},{"rank_in_archive_order":14,"model":"BERT+WWM+MT (single model)","metrics":{"EM":"88.650","F1":"94.393"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":15,"model":"Tuned BERT-1seq Large Cased (single model)","metrics":{"EM":"87.465","F1":"93.294"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":16,"model":"LinkBERT (large)","metrics":{"EM":"87.45","F1":"92.7"},"uses_additional_data":false,"paper_date":"2022-03-29","paper":"/paper/linkbert-pretraining-language-models-with","paper_url":"https://arxiv.org/abs/2203.15827v1","paper_title":"LinkBERT: Pretraining Language Models with Document Links","code":"https://github.com/michiyasunaga/LinkBERT","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":14,"n_samples":14,"n_pointer_only_licence":0}},{"rank_in_archive_order":17,"model":"BERT (ensemble)","metrics":{"EM":"87.433","F1":"93.160"},"uses_additional_data":false,"paper_date":"2018-10-11","paper":"/paper/bert-pre-training-of-deep-bidirectional","paper_url":"https://arxiv.org/abs/1810.04805v2","paper_title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","code":"https://github.com/huggingface/transformers","n_code_links":534,"syntology":{"n_ran":204,"n_unverified":455,"n_samples":659,"n_pointer_only_licence":149}},{"rank_in_archive_order":18,"model":"BERT-LARGE (Ensemble+TriviaQA)","metrics":{"EM":"87.4","F1":"93.2"},"uses_additional_data":false,"paper_date":"2018-10-11","paper":"/paper/bert-pre-training-of-deep-bidirectional","paper_url":"https://arxiv.org/abs/1810.04805v2","paper_title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","code":"https://github.com/huggingface/transformers","n_code_links":534,"syntology":{"n_ran":204,"n_unverified":455,"n_samples":659,"n_pointer_only_licence":149}},{"rank_in_archive_order":19,"model":"ATB (single model)","metrics":{"EM":"86.940","F1":"92.641"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":20,"model":"Tuned BERT Large Cased (single model)","metrics":{"EM":"86.521","F1":"92.617"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":21,"model":"BERT+MT (single model)","metrics":{"EM":"86.458","F1":"92.645"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":22,"model":"Knowledge-enhanced BERT (single model)","metrics":{"EM":"85.944","F1":"92.425"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":23,"model":"KT-NET (single model)","metrics":{"EM":"85.944","F1":"92.425"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":24,"model":"ST_bl","metrics":{"EM":"85.430","F1":"91.976"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":25,"model":"nlnet (ensemble)","metrics":{"EM":"85.356","F1":"91.202"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":26,"model":"EL-BERT (single model)","metrics":{"EM":"85.335","F1":"91.807"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":27,"model":"BISAN (single model)","metrics":{"EM":"85.314","F1":"91.756"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":28,"model":"BERT+Sparse-Transformer","metrics":{"EM":"85.125","F1":"91.623"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":29,"model":"BERT (single model)","metrics":{"EM":"85.083","F1":"91.835"},"uses_additional_data":true,"paper_date":"2018-10-11","paper":"/paper/bert-pre-training-of-deep-bidirectional","paper_url":"https://arxiv.org/abs/1810.04805v2","paper_title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","code":"https://github.com/huggingface/transformers","n_code_links":534,"syntology":{"n_ran":204,"n_unverified":455,"n_samples":659,"n_pointer_only_licence":149}},{"rank_in_archive_order":30,"model":"DPN (single model)","metrics":{"EM":"84.978","F1":"92.019"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":31,"model":"BERT-uncased (single model)","metrics":{"EM":"84.926","F1":"91.932"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":32,"model":"WD (single model)","metrics":{"EM":"84.402","F1":"90.561"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":33,"model":"Original BERT Large Cased (single model)","metrics":{"EM":"84.328","F1":"91.281"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":34,"model":"MARS (ensemble)","metrics":{"EM":"83.982","F1":"89.796"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":35,"model":"Common-sense Governed BERT-123 (single model)","metrics":{"EM":"83.930","F1":"90.613"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":36,"model":"WD1 (single model)","metrics":{"EM":"83.804","F1":"90.429"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":37,"model":"nlnet (single model)","metrics":{"EM":"83.468","F1":"90.133"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":38,"model":"Pytalk + Stanza + BERT (single model)","metrics":{"EM":"83.426","F1":"89.218"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":39,"model":"Reinforced Mnemonic Reader + A2D (ensemble model)","metrics":{"EM":"82.849","F1":"88.764"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":40,"model":"BERT-Base mod (single model)","metrics":{"EM":"82.681","F1":"89.379"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":41,"model":"r-net+ (ensemble)","metrics":{"EM":"82.650","F1":"88.493"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":42,"model":"Hybrid AoA Reader (ensemble)","metrics":{"EM":"82.482","F1":"89.281"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":43,"model":"QANet (single)","metrics":{"EM":"82.471","F1":"89.306"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":44,"model":"SLQA+ (ensemble)","metrics":{"EM":"82.440","F1":"88.607"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":45,"model":"Reinforced Mnemonic Reader (ensemble model)","metrics":{"EM":"82.283","F1":"88.533"},"uses_additional_data":true,"paper_date":"2017-05-08","paper":"/paper/reinforced-mnemonic-reader-for-machine","paper_url":"http://arxiv.org/abs/1705.02798v6","paper_title":"Reinforced Mnemonic Reader for Machine Reading Comprehension","code":"https://github.com/HKUST-KnowComp/MnemonicReader","n_code_links":3,"syntology":null},{"rank_in_archive_order":46,"model":"r-net (ensemble)","metrics":{"EM":"82.136","F1":"88.126"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":47,"model":"BERT (single model)","metrics":{"EM":"82.062","F1":"88.947"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":48,"model":"AttentionReader+ (ensemble)","metrics":{"EM":"81.790","F1":"88.163"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":49,"model":"MMIPN","metrics":{"EM":"81.580","F1":"88.948"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":50,"model":"BERT - 6 Layers","metrics":{"EM":"81.5","F1":"88.5"},"uses_additional_data":false,"paper_date":"2021-12-01","paper":"/paper/information-theoretic-representation","paper_url":"https://arxiv.org/abs/2112.00459v3","paper_title":"Information Theoretic Representation Distillation","code":"https://github.com/roymiles/ITRD","n_code_links":1,"syntology":null},{"rank_in_archive_order":51,"model":"KACTEIL-MRC(GF-Net+) (ensemble)","metrics":{"EM":"81.496","F1":"87.557"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":52,"model":"Reinforced Mnemonic Reader + A2D + DA (single model)","metrics":{"EM":"81.401","F1":"88.122"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":53,"model":"ARSG-BERT (single model)","metrics":{"EM":"81.307","F1":"88.909"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":54,"model":"BERT-COMPOUND-DSS (single model)","metrics":{"EM":"81.045","F1":"87.999"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":55,"model":"BiDAF + Self Attention + ELMo (ensemble)","metrics":{"EM":"81.003","F1":"87.432"},"uses_additional_data":false,"paper_date":"2018-02-15","paper":"/paper/deep-contextualized-word-representations","paper_url":"http://arxiv.org/abs/1802.05365v2","paper_title":"Deep contextualized word representations","code":"https://github.com/flairNLP/flair","n_code_links":46,"syntology":{"n_ran":23,"n_unverified":35,"n_samples":58,"n_pointer_only_licence":25}},{"rank_in_archive_order":56,"model":"BiDAF + Self Attention + ELMo (ensemble)","metrics":{"EM":"81.003","F1":"87.432"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":57,"model":"BERT-COMPOUND (single model)","metrics":{"EM":"80.720","F1":"87.758"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":58,"model":"mBERT + Task Adapter (Single)","metrics":{"EM":"80.667","F1":"88.169"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":59,"model":"AVIQA+ (ensemble)","metrics":{"EM":"80.615","F1":"87.311"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":60,"model":"Reinforced Mnemonic Reader + A2D (single model)","metrics":{"EM":"80.489","F1":"87.454"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":61,"model":"SLQA+","metrics":{"EM":"80.436","F1":"87.021"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":62,"model":"{EAZI} (ensemble)","metrics":{"EM":"80.436","F1":"86.912"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":63,"model":"EAZI+ (ensemble)","metrics":{"EM":"80.426","F1":"86.912"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":64,"model":"DNET (ensemble)","metrics":{"EM":"80.164","F1":"86.721"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":65,"model":"Hybrid AoA Reader (single model)","metrics":{"EM":"80.027","F1":"87.288"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":66,"model":"BiDAF + Self Attention + ELMo + A2D (single model)","metrics":{"EM":"79.996","F1":"86.711"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":67,"model":"r-net+ (single model)","metrics":{"EM":"79.901","F1":"86.536"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":68,"model":"batch (single model)","metrics":{"EM":"79.859","F1":"88.263"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":69,"model":"MAMCN+ (single model)","metrics":{"EM":"79.692","F1":"86.727"},"uses_additional_data":false,"paper_date":"2018-07-01","paper":"/paper/a-multi-stage-memory-augmented-neural-network","paper_url":"https://aclanthology.org/W18-2603","paper_title":"A Multi-Stage Memory Augmented Neural Network for Machine Reading Comprehension","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":70,"model":"MAMCN+ (single model)","metrics":{"EM":"79.692","F1":"86.727"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":71,"model":"SAN (ensemble model)","metrics":{"EM":"79.608","F1":"86.496"},"uses_additional_data":false,"paper_date":"2017-12-10","paper":"/paper/stochastic-answer-networks-for-machine","paper_url":"http://arxiv.org/abs/1712.03556v2","paper_title":"Stochastic Answer Networks for Machine Reading Comprehension","code":"https://github.com/kevinduh/san_mrc","n_code_links":6,"syntology":null},{"rank_in_archive_order":72,"model":"BERT-INDEPENDENT-DSS-FILTERED (single model)","metrics":{"EM":"79.597","F1":"87.374"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":73,"model":"Reinforced Mnemonic Reader (single model)","metrics":{"EM":"79.545","F1":"86.654"},"uses_additional_data":false,"paper_date":"2017-05-08","paper":"/paper/reinforced-mnemonic-reader-for-machine","paper_url":"http://arxiv.org/abs/1705.02798v6","paper_title":"Reinforced Mnemonic Reader for Machine Reading Comprehension","code":"https://github.com/HKUST-KnowComp/MnemonicReader","n_code_links":3,"syntology":null},{"rank_in_archive_order":74,"model":"SLQA+ (single model)","metrics":{"EM":"79.199","F1":"86.590"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":75,"model":"Interactive AoA Reader+ (ensemble)","metrics":{"EM":"79.083","F1":"86.450"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":76,"model":"MIR-MRC(F-Net) (single model)","metrics":{"EM":"79.083","F1":"86.288"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":77,"model":"KACTEIL-MRC(GF-Net+Distillation) (single model)","metrics":{"EM":"79.083","F1":"86.288"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":78,"model":"KACTEIL-MRC (GF-Net+Distillation)","metrics":{"EM":"79.083","F1":"86.288"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":79,"model":"MDReader","metrics":{"EM":"79.031","F1":"86.006"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":80,"model":"FusionNet (ensemble)","metrics":{"EM":"78.978","F1":"86.016"},"uses_additional_data":false,"paper_date":"2017-11-16","paper":"/paper/fusionnet-fusing-via-fully-aware-attention","paper_url":"http://arxiv.org/abs/1711.07341v2","paper_title":"FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension","code":"https://github.com/momohuang/FusionNet-NLI","n_code_links":3,"syntology":null},{"rank_in_archive_order":81,"model":"DCN+ (ensemble)","metrics":{"EM":"78.852","F1":"85.996"},"uses_additional_data":false,"paper_date":"2017-10-31","paper":"/paper/dcn-mixed-objective-and-deep-residual","paper_url":"http://arxiv.org/abs/1711.00106v2","paper_title":"DCN+: Mixed Objective and Deep Residual Coattention for Question Answering","code":"https://github.com/lmn-extracts/dcn_plus","n_code_links":1,"syntology":null},{"rank_in_archive_order":82,"model":"KACTEIL-MRC(GF-Net+) (single model)","metrics":{"EM":"78.664","F1":"85.780"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":83,"model":"KACTEIL-MRC (GF-Net+)","metrics":{"EM":"78.664","F1":"85.780"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":84,"model":"BERT-INDEPENDENT (single model)","metrics":{"EM":"78.653","F1":"86.663"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":85,"model":"BiDAF + Self Attention + ELMo (single model)","metrics":{"EM":"78.58","F1":"85.833"},"uses_additional_data":false,"paper_date":"2018-02-15","paper":"/paper/deep-contextualized-word-representations","paper_url":"http://arxiv.org/abs/1802.05365v2","paper_title":"Deep contextualized word representations","code":"https://github.com/flairNLP/flair","n_code_links":46,"syntology":{"n_ran":23,"n_unverified":35,"n_samples":58,"n_pointer_only_licence":25}},{"rank_in_archive_order":86,"model":"BiDAF + Self Attention + ELMo (single model)","metrics":{"EM":"78.580","F1":"85.833"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":87,"model":"aviqa (ensemble)","metrics":{"EM":"78.496","F1":"85.469"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":88,"model":"KakaoNet (single model)","metrics":{"EM":"78.401","F1":"85.724"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":89,"model":"SLQA(ensemble)","metrics":{"EM":"78.328","F1":"85.682"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":90,"model":"SLQA (ensemble)","metrics":{"EM":"78.328","F1":"85.682"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":91,"model":"MEMEN  (single model)","metrics":{"EM":"78.234","F1":"85.344"},"uses_additional_data":false,"paper_date":"2017-07-28","paper":"/paper/memen-multi-layer-embedding-with-memory","paper_url":"http://arxiv.org/abs/1707.09098v1","paper_title":"MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":92,"model":"MEMEN (single model)","metrics":{"EM":"78.234","F1":"85.344"},"uses_additional_data":false,"paper_date":"2017-07-28","paper":"/paper/memen-multi-layer-embedding-with-memory","paper_url":"http://arxiv.org/abs/1707.09098v1","paper_title":"MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":93,"model":"BiDAF++ with pair2vec (single model)","metrics":{"EM":"78.223","F1":"85.535"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":94,"model":"MDReader0","metrics":{"EM":"78.171","F1":"85.543"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":95,"model":"test","metrics":{"EM":"78.087","F1":"85.348"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":96,"model":"Interactive AoA Reader (ensemble)","metrics":{"EM":"77.845","F1":"85.297"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":97,"model":"BERT - 3 Layers","metrics":{"EM":"77.7","F1":"85.8"},"uses_additional_data":false,"paper_date":"2021-12-01","paper":"/paper/information-theoretic-representation","paper_url":"https://arxiv.org/abs/2112.00459v3","paper_title":"Information Theoretic Representation Distillation","code":"https://github.com/roymiles/ITRD","n_code_links":1,"syntology":null},{"rank_in_archive_order":98,"model":"DNET (single model)","metrics":{"EM":"77.646","F1":"84.905"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":99,"model":"RaSoR + TR + LM (single model)","metrics":{"EM":"77.583","F1":"84.163"},"uses_additional_data":false,"paper_date":"2017-12-10","paper":"/paper/contextualized-word-representations-for","paper_url":"http://arxiv.org/abs/1712.03609v4","paper_title":"Contextualized Word Representations for Reading Comprehension","code":"https://github.com/shimisalant/CWR","n_code_links":1,"syntology":null},{"rank_in_archive_order":100,"model":"BiDAF++ (single model)","metrics":{"EM":"77.573","F1":"84.858"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":101,"model":"AttentionReader+ (single)","metrics":{"EM":"77.342","F1":"84.925"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":102,"model":"Jenga (ensemble)","metrics":{"EM":"77.237","F1":"84.466"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":103,"model":"{gqa} (single model)","metrics":{"EM":"77.090","F1":"83.931"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":104,"model":"Conductor-net (ensemble)","metrics":{"EM":"76.996","F1":"84.630"},"uses_additional_data":false,"paper_date":"2017-10-28","paper":"/paper/phase-conductor-on-multi-layered-attentions","paper_url":"http://arxiv.org/abs/1710.10504v2","paper_title":"Phase Conductor on Multi-layered Attentions for Machine Comprehension","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":105,"model":"MARS (single model)","metrics":{"EM":"76.859","F1":"84.739"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":106,"model":"SAN (single model)","metrics":{"EM":"76.828","F1":"84.396"},"uses_additional_data":false,"paper_date":"2017-12-10","paper":"/paper/stochastic-answer-networks-for-machine","paper_url":"http://arxiv.org/abs/1712.03556v2","paper_title":"Stochastic Answer Networks for Machine Reading Comprehension","code":"https://github.com/kevinduh/san_mrc","n_code_links":6,"syntology":null},{"rank_in_archive_order":107,"model":"VS^3-NET (single model)","metrics":{"EM":"76.775","F1":"84.491"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":108,"model":"r-net (single model)","metrics":{"EM":"76.461","F1":"84.265"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":109,"model":"r-net (single model)","metrics":{"EM":"76.461","F1":"84.265"},"uses_additional_data":false,"paper_date":"2017-07-01","paper":"/paper/gated-self-matching-networks-for-reading","paper_url":"https://aclanthology.org/P17-1018","paper_title":"Gated Self-Matching Networks for Reading Comprehension and Question Answering","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":110,"model":"FRC (single model)","metrics":{"EM":"76.240","F1":"84.599"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":111,"model":"QANet + data augmentation ×3","metrics":{"EM":"76.2","F1":"84.6"},"uses_additional_data":false,"paper_date":"2018-04-23","paper":"/paper/qanet-combining-local-convolution-with-global","paper_url":"http://arxiv.org/abs/1804.09541v1","paper_title":"QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension","code":"https://github.com/BangLiu/QANet-PyTorch","n_code_links":15,"syntology":{"n_ran":7,"n_unverified":12,"n_samples":19,"n_pointer_only_licence":2}},{"rank_in_archive_order":112,"model":"Conductor-net (ensemble)","metrics":{"EM":"76.146","F1":"83.991"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":113,"model":"KAR (single model)","metrics":{"EM":"76.125","F1":"83.538"},"uses_additional_data":false,"paper_date":"2018-09-10","paper":"/paper/exploring-machine-reading-comprehension-with","paper_url":"https://arxiv.org/abs/1809.03449v3","paper_title":"Explicit Utilization of General Knowledge in Machine Reading Comprehension","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":114,"model":"smarnet (ensemble)","metrics":{"EM":"75.989","F1":"83.475"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":115,"model":"FusionNet (single model)","metrics":{"EM":"75.968","F1":"83.900"},"uses_additional_data":false,"paper_date":"2017-11-16","paper":"/paper/fusionnet-fusing-via-fully-aware-attention","paper_url":"http://arxiv.org/abs/1711.07341v2","paper_title":"FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension","code":"https://github.com/momohuang/FusionNet-NLI","n_code_links":3,"syntology":null},{"rank_in_archive_order":116,"model":"AVIQA-v2 (single model)","metrics":{"EM":"75.926","F1":"83.305"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":117,"model":"Interactive AoA Reader+ (single model)","metrics":{"EM":"75.821","F1":"83.843"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":118,"model":"RaSoR + TR (single model)","metrics":{"EM":"75.789","F1":"83.261"},"uses_additional_data":false,"paper_date":"2017-12-10","paper":"/paper/contextualized-word-representations-for","paper_url":"http://arxiv.org/abs/1712.03609v4","paper_title":"Contextualized Word Representations for Reading Comprehension","code":"https://github.com/shimisalant/CWR","n_code_links":1,"syntology":null},{"rank_in_archive_order":119,"model":"MEMEN (ensemble)","metrics":{"EM":"75.370","F1":"82.658"},"uses_additional_data":false,"paper_date":"2017-07-28","paper":"/paper/memen-multi-layer-embedding-with-memory","paper_url":"http://arxiv.org/abs/1707.09098v1","paper_title":"MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":120,"model":"Mixed model (ensemble)","metrics":{"EM":"75.265","F1":"82.769"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":121,"model":"two-attention-self-attention (ensemble)","metrics":{"EM":"75.223","F1":"82.716"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":122,"model":"Kbs (single model)","metrics":{"EM":"75.034","F1":"83.405"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":123,"model":"ReasoNet (ensemble)","metrics":{"EM":"75.034","F1":"82.552"},"uses_additional_data":true,"paper_date":"2016-09-17","paper":"/paper/reasonet-learning-to-stop-reading-in-machine","paper_url":"http://arxiv.org/abs/1609.05284v3","paper_title":"ReasoNet: Learning to Stop Reading in Machine Comprehension","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":124,"model":"EfficientQA 125M","metrics":{"EM":"74.9","F1":"83.1"},"uses_additional_data":false,"paper_date":"2021-01-06","paper":"/paper/efficientqa-a-roberta-based-phrase-indexed","paper_url":"https://arxiv.org/abs/2101.02157v2","paper_title":"EfficientQA : a RoBERTa Based Phrase-Indexed Question-Answering System","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":125,"model":"DCN+ (single model)","metrics":{"EM":"74.866","F1":"82.806"},"uses_additional_data":false,"paper_date":"2017-10-31","paper":"/paper/dcn-mixed-objective-and-deep-residual","paper_url":"http://arxiv.org/abs/1711.00106v2","paper_title":"DCN+: Mixed Objective and Deep Residual Coattention for Question Answering","code":"https://github.com/lmn-extracts/dcn_plus","n_code_links":1,"syntology":null},{"rank_in_archive_order":126,"model":"eeAttNet (single model)","metrics":{"EM":"74.604","F1":"82.501"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":127,"model":"SLQA (single model)","metrics":{"EM":"74.489","F1":"82.815"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":128,"model":"Conductor-net (single model)","metrics":{"EM":"74.405","F1":"82.742"},"uses_additional_data":false,"paper_date":"2017-10-28","paper":"/paper/phase-conductor-on-multi-layered-attentions","paper_url":"http://arxiv.org/abs/1710.10504v2","paper_title":"Phase Conductor on Multi-layered Attentions for Machine Comprehension","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":129,"model":"Mnemonic Reader (ensemble)","metrics":{"EM":"74.268","F1":"82.371"},"uses_additional_data":false,"paper_date":"2017-05-08","paper":"/paper/reinforced-mnemonic-reader-for-machine","paper_url":"http://arxiv.org/abs/1705.02798v6","paper_title":"Reinforced Mnemonic Reader for Machine Reading Comprehension","code":"https://github.com/HKUST-KnowComp/MnemonicReader","n_code_links":3,"syntology":null},{"rank_in_archive_order":130,"model":"S^3-Net (ensemble)","metrics":{"EM":"74.121","F1":"82.342"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":131,"model":"SEDT (ensemble model)","metrics":{"EM":"74.090","F1":"81.761"},"uses_additional_data":false,"paper_date":"2017-03-02","paper":"/paper/structural-embedding-of-syntactic-trees-for","paper_url":"http://arxiv.org/abs/1703.00572v3","paper_title":"Structural Embedding of Syntactic Trees for Machine Comprehension","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":132,"model":"SSAE (ensemble)","metrics":{"EM":"74.080","F1":"81.665"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":133,"model":"Multi-Perspective Matching (ensemble)","metrics":{"EM":"73.765","F1":"81.257"},"uses_additional_data":false,"paper_date":"2016-12-13","paper":"/paper/multi-perspective-context-matching-for","paper_url":"http://arxiv.org/abs/1612.04211v1","paper_title":"Multi-Perspective Context Matching for Machine Comprehension","code":"https://github.com/bloomsburyai/question-generation","n_code_links":1,"syntology":null},{"rank_in_archive_order":134,"model":"BiDAF (ensemble)","metrics":{"EM":"73.744","F1":"81.525"},"uses_additional_data":false,"paper_date":"2016-11-05","paper":"/paper/bidirectional-attention-flow-for-machine","paper_url":"http://arxiv.org/abs/1611.01603v6","paper_title":"Bidirectional Attention Flow for Machine Comprehension","code":"https://github.com/allenai/bi-att-flow","n_code_links":27,"syntology":{"n_ran":8,"n_unverified":3,"n_samples":11,"n_pointer_only_licence":7}},{"rank_in_archive_order":135,"model":"SEDT+BiDAF (ensemble)","metrics":{"EM":"73.723","F1":"81.530"},"uses_additional_data":false,"paper_date":"2017-03-02","paper":"/paper/structural-embedding-of-syntactic-trees-for","paper_url":"http://arxiv.org/abs/1703.00572v3","paper_title":"Structural Embedding of Syntactic Trees for Machine Comprehension","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":136,"model":"Interactive AoA Reader (single model)","metrics":{"EM":"73.639","F1":"81.931"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":137,"model":"Jenga (single model)","metrics":{"EM":"73.303","F1":"81.754"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":138,"model":"Conductor-net (single)","metrics":{"EM":"73.240","F1":"81.933"},"uses_additional_data":false,"paper_date":"2017-10-28","paper":"/paper/phase-conductor-on-multi-layered-attentions","paper_url":"http://arxiv.org/abs/1710.10504v2","paper_title":"Phase Conductor on Multi-layered Attentions for Machine Comprehension","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":139,"model":"jNet (ensemble)","metrics":{"EM":"73.010","F1":"81.517"},"uses_additional_data":false,"paper_date":"2017-03-14","paper":"/paper/exploring-question-understanding-and","paper_url":"http://arxiv.org/abs/1703.04617v2","paper_title":"Exploring Question Understanding and Adaptation in Neural-Network-Based Question Answering","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":140,"model":"T-gating (ensemble)","metrics":{"EM":"72.758","F1":"81.001"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":141,"model":"two-attention-self-attention (single model)","metrics":{"EM":"72.600","F1":"81.011"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":142,"model":"Conductor-net (single)","metrics":{"EM":"72.590","F1":"81.415"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":143,"model":"AVIQA (single model)","metrics":{"EM":"72.485","F1":"80.550"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":144,"model":"BiDAF + Self Attention (single model)","metrics":{"EM":"72.139","F1":"81.048"},"uses_additional_data":false,"paper_date":"2017-10-29","paper":"/paper/simple-and-effective-multi-paragraph-reading","paper_url":"http://arxiv.org/abs/1710.10723v2","paper_title":"Simple and Effective Multi-Paragraph Reading Comprehension","code":"https://github.com/allenai/document-qa","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":4,"n_samples":4,"n_pointer_only_licence":0}},{"rank_in_archive_order":145,"model":"S^3-Net (single model)","metrics":{"EM":"71.908","F1":"81.023"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":146,"model":"QFASE","metrics":{"EM":"71.898","F1":"79.989"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":147,"model":"attention+self-attention (single model)","metrics":{"EM":"71.698","F1":"80.462"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":148,"model":"Dynamic Coattention Networks (ensemble)","metrics":{"EM":"71.625","F1":"80.383"},"uses_additional_data":false,"paper_date":"2016-11-05","paper":"/paper/dynamic-coattention-networks-for-question","paper_url":"http://arxiv.org/abs/1611.01604v4","paper_title":"Dynamic Coattention Networks For Question Answering","code":"https://github.com/lmn-extracts/dcn_plus","n_code_links":6,"syntology":null},{"rank_in_archive_order":149,"model":"smarnet (single model)","metrics":{"EM":"71.415","F1":"80.160"},"uses_additional_data":false,"paper_date":"2017-10-08","paper":"/paper/smarnet-teaching-machines-to-read-and","paper_url":"http://arxiv.org/abs/1710.02772v1","paper_title":"Smarnet: Teaching Machines to Read and Comprehend Like Human","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":150,"model":"SRU","metrics":{"EM":"71.4","F1":"80.2","Hardware Burden":"4G"},"uses_additional_data":false,"paper_date":"2017-09-08","paper":"/paper/simple-recurrent-units-for-highly","paper_url":"http://arxiv.org/abs/1709.02755v5","paper_title":"Simple Recurrent Units for Highly Parallelizable Recurrence","code":"https://github.com/aymericdamien/TopDeepLearning","n_code_links":11,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":0}},{"rank_in_archive_order":151,"model":"AttReader (single)","metrics":{"EM":"71.373","F1":"79.725"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":152,"model":"DCN + Char + CoVe","metrics":{"EM":"71.3","F1":"79.9"},"uses_additional_data":false,"paper_date":"2017-08-01","paper":"/paper/learned-in-translation-contextualized-word","paper_url":"http://arxiv.org/abs/1708.00107v2","paper_title":"Learned in Translation: Contextualized Word Vectors","code":"https://github.com/salesforce/cove","n_code_links":5,"syntology":{"n_ran":3,"n_unverified":0,"n_samples":3,"n_pointer_only_licence":2}},{"rank_in_archive_order":153,"model":"M-NET (single)","metrics":{"EM":"71.016","F1":"79.835"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":154,"model":"Mnemonic Reader (single model)","metrics":{"EM":"70.995","F1":"80.146"},"uses_additional_data":false,"paper_date":"2017-05-08","paper":"/paper/reinforced-mnemonic-reader-for-machine","paper_url":"http://arxiv.org/abs/1705.02798v6","paper_title":"Reinforced Mnemonic Reader for Machine Reading Comprehension","code":"https://github.com/HKUST-KnowComp/MnemonicReader","n_code_links":3,"syntology":null},{"rank_in_archive_order":155,"model":"MAMCN (single model)","metrics":{"EM":"70.985","F1":"79.939"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":156,"model":"FastQAExt","metrics":{"EM":"70.849","F1":"78.857"},"uses_additional_data":false,"paper_date":"2017-03-14","paper":"/paper/making-neural-qa-as-simple-as-possible-but","paper_url":"http://arxiv.org/abs/1703.04816v3","paper_title":"Making Neural QA as Simple as Possible but not Simpler","code":"https://github.com/uclmr/jack","n_code_links":3,"syntology":{"n_ran":0,"n_unverified":2,"n_samples":2,"n_pointer_only_licence":0}},{"rank_in_archive_order":157,"model":"RaSoR (single model)","metrics":{"EM":"70.849","F1":"78.741"},"uses_additional_data":false,"paper_date":"2016-11-04","paper":"/paper/learning-recurrent-span-representations-for","paper_url":"http://arxiv.org/abs/1611.01436v2","paper_title":"Learning Recurrent Span Representations for Extractive Question Answering","code":"https://github.com/shimisalant/RaSoR","n_code_links":2,"syntology":null},{"rank_in_archive_order":158,"model":"Document Reader (single model)","metrics":{"EM":"70.733","F1":"79.353"},"uses_additional_data":false,"paper_date":"2017-03-31","paper":"/paper/reading-wikipedia-to-answer-open-domain","paper_url":"http://arxiv.org/abs/1704.00051v2","paper_title":"Reading Wikipedia to Answer Open-Domain Questions","code":"https://github.com/facebookresearch/ParlAI","n_code_links":10,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":1}},{"rank_in_archive_order":159,"model":"Ruminating Reader (single model)","metrics":{"EM":"70.639","F1":"79.456"},"uses_additional_data":false,"paper_date":"2017-04-24","paper":"/paper/ruminating-reader-reasoning-with-gated-multi","paper_url":"http://arxiv.org/abs/1704.07415v1","paper_title":"Ruminating Reader: Reasoning with Gated Multi-Hop Attention","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":160,"model":"jNet (single model)","metrics":{"EM":"70.607","F1":"79.821"},"uses_additional_data":false,"paper_date":"2017-03-14","paper":"/paper/exploring-question-understanding-and","paper_url":"http://arxiv.org/abs/1703.04617v2","paper_title":"Exploring Question Understanding and Adaptation in Neural-Network-Based Question Answering","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":161,"model":"ReasoNet (single model)","metrics":{"EM":"70.555","F1":"79.364"},"uses_additional_data":false,"paper_date":"2016-09-17","paper":"/paper/reasonet-learning-to-stop-reading-in-machine","paper_url":"http://arxiv.org/abs/1609.05284v3","paper_title":"ReasoNet: Learning to Stop Reading in Machine Comprehension","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":162,"model":"Multi-Perspective Matching (single model)","metrics":{"EM":"70.387","F1":"78.784"},"uses_additional_data":false,"paper_date":"2016-12-13","paper":"/paper/multi-perspective-context-matching-for","paper_url":"http://arxiv.org/abs/1612.04211v1","paper_title":"Multi-Perspective Context Matching for Machine Comprehension","code":"https://github.com/bloomsburyai/question-generation","n_code_links":1,"syntology":null},{"rank_in_archive_order":163,"model":"SimpleBaseline (single model)","metrics":{"EM":"69.600","F1":"78.236"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":164,"model":"SSR-BiDAF","metrics":{"EM":"69.443","F1":"78.358"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":165,"model":"SEDT+BiDAF (single model)","metrics":{"EM":"68.478","F1":"77.971"},"uses_additional_data":false,"paper_date":"2017-03-02","paper":"/paper/structural-embedding-of-syntactic-trees-for","paper_url":"http://arxiv.org/abs/1703.00572v3","paper_title":"Structural Embedding of Syntactic Trees for Machine Comprehension","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":166,"model":"FastQA","metrics":{"EM":"68.436","F1":"77.070"},"uses_additional_data":false,"paper_date":"2017-03-14","paper":"/paper/making-neural-qa-as-simple-as-possible-but","paper_url":"http://arxiv.org/abs/1703.04816v3","paper_title":"Making Neural QA as Simple as Possible but not Simpler","code":"https://github.com/uclmr/jack","n_code_links":3,"syntology":{"n_ran":0,"n_unverified":2,"n_samples":2,"n_pointer_only_licence":0}},{"rank_in_archive_order":167,"model":"PQMN (single model)","metrics":{"EM":"68.331","F1":"77.783"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":168,"model":"SEDT (single model)","metrics":{"EM":"68.163","F1":"77.527"},"uses_additional_data":false,"paper_date":"2017-03-02","paper":"/paper/structural-embedding-of-syntactic-trees-for","paper_url":"http://arxiv.org/abs/1703.00572v3","paper_title":"Structural Embedding of Syntactic Trees for Machine Comprehension","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":169,"model":"T-gating (single model)","metrics":{"EM":"68.132","F1":"77.569"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":170,"model":"BiDAF (single model)","metrics":{"EM":"67.974","F1":"77.323"},"uses_additional_data":false,"paper_date":"2016-11-05","paper":"/paper/bidirectional-attention-flow-for-machine","paper_url":"http://arxiv.org/abs/1611.01603v6","paper_title":"Bidirectional Attention Flow for Machine Comprehension","code":"https://github.com/allenai/bi-att-flow","n_code_links":27,"syntology":{"n_ran":8,"n_unverified":3,"n_samples":11,"n_pointer_only_licence":7}},{"rank_in_archive_order":171,"model":"Match-LSTM with Ans-Ptr (Boundary) (ensemble)","metrics":{"EM":"67.901","F1":"77.022"},"uses_additional_data":false,"paper_date":"2016-08-29","paper":"/paper/machine-comprehension-using-match-lstm-and","paper_url":"http://arxiv.org/abs/1608.07905v2","paper_title":"Machine Comprehension Using Match-LSTM and Answer Pointer","code":"https://github.com/baidu/DuReader","n_code_links":5,"syntology":{"n_ran":3,"n_unverified":0,"n_samples":3,"n_pointer_only_licence":3}},{"rank_in_archive_order":172,"model":"FABIR","metrics":{"EM":"67.744","F1":"77.605"},"uses_additional_data":false,"paper_date":"2018-10-22","paper":"/paper/a-fully-attention-based-information-retriever","paper_url":"http://arxiv.org/abs/1810.09580v1","paper_title":"A Fully Attention-Based Information Retriever","code":"https://github.com/AlCorreia/FABIR","n_code_links":1,"syntology":null},{"rank_in_archive_order":173,"model":"AllenNLP BiDAF (single model)","metrics":{"EM":"67.618","F1":"77.151"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":174,"model":"BIDAF-COMPOUND-DSS (single model)","metrics":{"EM":"67.544","F1":"76.429"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":175,"model":"Iterative Co-attention Network","metrics":{"EM":"67.502","F1":"76.786"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":176,"model":"newtest","metrics":{"EM":"66.527","F1":"75.787"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":177,"model":"BIDAF-INDEPENDENT-DSS (single model)","metrics":{"EM":"66.516","F1":"76.349"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":178,"model":"Dynamic Coattention Networks (single model)","metrics":{"EM":"66.233","F1":"75.896"},"uses_additional_data":false,"paper_date":"2016-11-05","paper":"/paper/dynamic-coattention-networks-for-question","paper_url":"http://arxiv.org/abs/1611.01604v4","paper_title":"Dynamic Coattention Networks For Question Answering","code":"https://github.com/lmn-extracts/dcn_plus","n_code_links":6,"syntology":null},{"rank_in_archive_order":179,"model":"BIDAF-COMPOUND (single model)","metrics":{"EM":"65.163","F1":"74.555"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":180,"model":"BIDAF-INDEPENDENT (single model)","metrics":{"EM":"64.932","F1":"74.594"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":181,"model":"Match-LSTM with Bi-Ans-Ptr (Boundary)","metrics":{"EM":"64.744","F1":"73.743"},"uses_additional_data":false,"paper_date":"2016-08-29","paper":"/paper/machine-comprehension-using-match-lstm-and","paper_url":"http://arxiv.org/abs/1608.07905v2","paper_title":"Machine Comprehension Using Match-LSTM and Answer Pointer","code":"https://github.com/baidu/DuReader","n_code_links":5,"syntology":{"n_ran":3,"n_unverified":0,"n_samples":3,"n_pointer_only_licence":3}},{"rank_in_archive_order":182,"model":"Unnamed submission by ravioncodalab","metrics":{"EM":"64.439","F1":"73.921"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":183,"model":"OTF dict+spelling (single)","metrics":{"EM":"64.083","F1":"73.056"},"uses_additional_data":false,"paper_date":"2017-06-01","paper":"/paper/learning-to-compute-word-embeddings-on-the","paper_url":"http://arxiv.org/abs/1706.00286v3","paper_title":"Learning to Compute Word Embeddings On the Fly","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":184,"model":"Attentive CNN context with LSTM","metrics":{"EM":"63.306","F1":"73.463"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":185,"model":"OTF spelling (single)","metrics":{"EM":"62.897","F1":"72.016"},"uses_additional_data":false,"paper_date":"2017-06-01","paper":"/paper/learning-to-compute-word-embeddings-on-the","paper_url":"http://arxiv.org/abs/1706.00286v3","paper_title":"Learning to Compute Word Embeddings On the Fly","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":186,"model":"OTF spelling+lemma (single)","metrics":{"EM":"62.604","F1":"71.968"},"uses_additional_data":false,"paper_date":"2017-06-01","paper":"/paper/learning-to-compute-word-embeddings-on-the","paper_url":"http://arxiv.org/abs/1706.00286v3","paper_title":"Learning to Compute Word Embeddings On the Fly","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":187,"model":"Dynamic Chunk Reader","metrics":{"EM":"62.499","F1":"70.956"},"uses_additional_data":false,"paper_date":"2016-10-31","paper":"/paper/end-to-end-answer-chunk-extraction-and","paper_url":"http://arxiv.org/abs/1610.09996v2","paper_title":"End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":188,"model":"Fine-Grained Gating","metrics":{"EM":"62.446","F1":"73.327"},"uses_additional_data":false,"paper_date":"2016-11-06","paper":"/paper/words-or-characters-fine-grained-gating-for","paper_url":"http://arxiv.org/abs/1611.01724v2","paper_title":"Words or Characters? Fine-grained Gating for Reading Comprehension","code":"https://github.com/kimiyoung/fg-gating","n_code_links":1,"syntology":null},{"rank_in_archive_order":189,"model":"RQA+IDR (single model)","metrics":{"EM":"61.145","F1":"71.389"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":190,"model":"RQA+IDR (single model)","metrics":{"EM":"61.145","F1":"71.389"},"uses_additional_data":false,"paper_date":"2020-05-06","paper":"/paper/harvesting-and-refining-question-answer-pairs","paper_url":"https://arxiv.org/abs/2005.02925v1","paper_title":"Harvesting and Refining Question-Answer Pairs for Unsupervised QA","code":"https://github.com/Neutralzz/RefQA","n_code_links":1,"syntology":null},{"rank_in_archive_order":191,"model":"Match-LSTM with Ans-Ptr (Boundary)","metrics":{"EM":"60.474","F1":"70.695"},"uses_additional_data":false,"paper_date":"2016-08-29","paper":"/paper/machine-comprehension-using-match-lstm-and","paper_url":"http://arxiv.org/abs/1608.07905v2","paper_title":"Machine Comprehension Using Match-LSTM and Answer Pointer","code":"https://github.com/baidu/DuReader","n_code_links":5,"syntology":{"n_ran":3,"n_unverified":0,"n_samples":3,"n_pointer_only_licence":3}},{"rank_in_archive_order":192,"model":"Unnamed submission by Will_Wu","metrics":{"EM":"59.058","F1":"69.436"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":193,"model":"RQA (single model)","metrics":{"EM":"55.827","F1":"65.467"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":194,"model":"RQA (single model)","metrics":{"EM":"55.827","F1":"65.467"},"uses_additional_data":false,"paper_date":"2020-05-06","paper":"/paper/harvesting-and-refining-question-answer-pairs","paper_url":"https://arxiv.org/abs/2005.02925v1","paper_title":"Harvesting and Refining Question-Answer Pairs for Unsupervised QA","code":"https://github.com/Neutralzz/RefQA","n_code_links":1,"syntology":null},{"rank_in_archive_order":195,"model":"Match-LSTM with Ans-Ptr (Sentence)","metrics":{"EM":"54.505","F1":"67.748"},"uses_additional_data":false,"paper_date":"2016-08-29","paper":"/paper/machine-comprehension-using-match-lstm-and","paper_url":"http://arxiv.org/abs/1608.07905v2","paper_title":"Machine Comprehension Using Match-LSTM and Answer Pointer","code":"https://github.com/baidu/DuReader","n_code_links":5,"syntology":{"n_ran":3,"n_unverified":0,"n_samples":3,"n_pointer_only_licence":3}},{"rank_in_archive_order":196,"model":"UQA (single model)","metrics":{"EM":"53.698","F1":"64.036"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":197,"model":"Unnamed submission by jinhyuklee","metrics":{"EM":"52.544","F1":"62.780"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":198,"model":"Unnamed submission by minjoon","metrics":{"EM":"52.533","F1":"62.757"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":199,"model":"UnsupervisedQA V1 (ensemble)","metrics":{"EM":"47.341","F1":"56.436"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":200,"model":"UnsupervisedQA V1 (single model)","metrics":{"EM":"44.215","F1":"54.723"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":201,"model":"QANet (single model)","metrics":{"EM":"12.273","F1":"13.211"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":202,"model":"","metrics":{"EM":"0.000","F1":"6.907"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":203,"model":"QANet (ensemble)","metrics":{"EM":"0.000","F1":"0.000"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":204,"model":"superman-new-des","metrics":{"EM":"0.000","F1":"0.000"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":205,"model":"WAHnGREA","metrics":{"EM":"0.000","F1":"0.000"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":206,"model":"superman-des","metrics":{"EM":"0.000","F1":"0.000"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":207,"model":"XLNet-deep (ensemble)","metrics":{"EM":"0.000","F1":"0.000"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":208,"model":"LUKE 483M","metrics":{"F1":"95.4"},"uses_additional_data":false,"paper_date":"2020-10-02","paper":"/paper/luke-deep-contextualized-entity","paper_url":"https://arxiv.org/abs/2010.01057v1","paper_title":"LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention","code":"https://github.com/huggingface/transformers","n_code_links":9,"syntology":{"n_ran":3,"n_unverified":7,"n_samples":10,"n_pointer_only_licence":0}},{"rank_in_archive_order":209,"model":"BART (TextBox 2.0)","metrics":{"Exact Match":"86.44","F1":"93.04"},"uses_additional_data":false,"paper_date":"2022-12-26","paper":"/paper/textbox-2-0-a-text-generation-library-with","paper_url":"https://arxiv.org/abs/2212.13005v1","paper_title":"TextBox 2.0: A Text Generation Library with Pre-trained Language Models","code":"https://github.com/RUCAIBox/TextBox","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":0}},{"rank_in_archive_order":210,"model":"BERT-LARGE (Single+TriviaQA)","metrics":{"F1":"91.8"},"uses_additional_data":false,"paper_date":"2018-10-11","paper":"/paper/bert-pre-training-of-deep-bidirectional","paper_url":"https://arxiv.org/abs/1810.04805v2","paper_title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","code":"https://github.com/huggingface/transformers","n_code_links":534,"syntology":{"n_ran":204,"n_unverified":455,"n_samples":659,"n_pointer_only_licence":149}},{"rank_in_archive_order":211,"model":"BERT-Large 32k batch size with AdamW","metrics":{"F1":"91.58"},"uses_additional_data":false,"paper_date":"2021-02-12","paper":"/paper/a-large-batch-optimizer-reality-check","paper_url":"https://arxiv.org/abs/2102.06356v3","paper_title":"A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizes","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":212,"model":"DyREX","metrics":{"F1":"91.01"},"uses_additional_data":false,"paper_date":"2022-10-26","paper":"/paper/dyrex-dynamic-query-representation-for","paper_url":"https://arxiv.org/abs/2210.15048v1","paper_title":"DyREx: Dynamic Query Representation for Extractive Question Answering","code":"https://github.com/urchade/dyrex","n_code_links":1,"syntology":{"n_ran":2,"n_unverified":1,"n_samples":3,"n_pointer_only_licence":3}},{"rank_in_archive_order":213,"model":"RuBERT","metrics":{"F1":"84.6"},"uses_additional_data":false,"paper_date":"2019-05-17","paper":"/paper/adaptation-of-deep-bidirectional-multilingual","paper_url":"https://arxiv.org/abs/1905.07213v1","paper_title":"Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language","code":"https://github.com/deepmipt/bert/tree/feat/multi_gpu","n_code_links":2,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":27,"rows_with_any_sample_ran":23,"distinct_papers_with_graph_line":16,"distinct_papers_with_any_sample_ran":13,"samples_over_distinct_papers":{"n_ran":269,"n_unverified":559,"n_samples":828,"n_pointer_only_licence":199,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":927,"n_unverified":1978,"n_samples":2905,"n_pointer_only_licence":687,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}