{"task":"Coreference Resolution","dataset":"OntoNotes","metric_names":["F1"],"rows":[{"id":52356,"task":"Coreference Resolution","parent_task":null,"dataset":"OntoNotes","model_name":"Maverick_mes","metrics":{"F1":"83.6"},"paper_url":"https://arxiv.org/abs/2407.21489v1","paper_title":"Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends","paper_date":"2024-07-31","code_links":[{"title":"sapienzanlp/maverick-coref","url":"https://github.com/sapienzanlp/maverick-coref"}],"metrics_order":"[\"F1\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":52357,"task":"Coreference Resolution","parent_task":null,"dataset":"OntoNotes","model_name":"seq2seq","metrics":{"F1":"83.3"},"paper_url":"https://arxiv.org/abs/2211.12142v1","paper_title":"Coreference Resolution through a seq2seq Transition-Based System","paper_date":"2022-11-22","code_links":[{"title":"google-research/google-research","url":"https://github.com/google-research/google-research"}],"metrics_order":"[\"F1\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":52358,"task":"Coreference Resolution","parent_task":null,"dataset":"OntoNotes","model_name":"ASP+T0-3B","metrics":{"F1":"82.3"},"paper_url":"https://arxiv.org/abs/2210.14698v2","paper_title":"Autoregressive Structured Prediction with Language Models","paper_date":"2022-10-26","code_links":[{"title":"lyutyuh/asp","url":"https://github.com/lyutyuh/asp"}],"metrics_order":"[\"F1\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":52359,"task":"Coreference Resolution","parent_task":null,"dataset":"OntoNotes","model_name":"caw-coref + RoBERTa","metrics":{"F1":"81.6"},"paper_url":"https://arxiv.org/abs/2310.06165v2","paper_title":"CAW-coref: Conjunction-Aware Word-level Coreference Resolution","paper_date":"2023-10-09","code_links":[{"title":"kareldo/wl-coref","url":"https://github.com/kareldo/wl-coref"}],"metrics_order":"[\"F1\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":52360,"task":"Coreference Resolution","parent_task":null,"dataset":"OntoNotes","model_name":"LingMess","metrics":{"F1":"81.4"},"paper_url":"https://arxiv.org/abs/2205.12644v3","paper_title":"LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution","paper_date":"2022-05-25","code_links":[{"title":"shon-otmazgin/fastcoref","url":"https://github.com/shon-otmazgin/fastcoref"},{"title":"shon-otmazgin/lingmess-coref","url":"https://github.com/shon-otmazgin/lingmess-coref"}],"metrics_order":"[\"F1\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":52361,"task":"Coreference Resolution","parent_task":null,"dataset":"OntoNotes","model_name":"wl-coref + RoBERTa","metrics":{"F1":"81"},"paper_url":"https://arxiv.org/abs/2109.04127v1","paper_title":"Word-Level Coreference Resolution","paper_date":"2021-09-09","code_links":[{"title":"vdobrovolskii/wl-coref","url":"https://github.com/vdobrovolskii/wl-coref"}],"metrics_order":"[\"F1\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":52362,"task":"Coreference Resolution","parent_task":null,"dataset":"OntoNotes","model_name":"U-MEM + Longformer","metrics":{"F1":"80.9"},"paper_url":"https://arxiv.org/abs/2208.14252v1","paper_title":"Efficient and Interpretable Neural Models for Entity Tracking","paper_date":"2022-08-30","code_links":[],"metrics_order":"[\"F1\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":52363,"task":"Coreference Resolution","parent_task":null,"dataset":"OntoNotes","model_name":"longdoc S (OntoNotes + 60k pseudo-singletons)","metrics":{"F1":"80.6"},"paper_url":"https://arxiv.org/abs/2109.09667v1","paper_title":"On Generalization in Coreference Resolution","paper_date":"2021-09-20","code_links":[{"title":"shtoshni/fast-coref","url":"https://github.com/shtoshni/fast-coref"},{"title":"shtoshni92/fast-coref","url":"https://github.com/shtoshni92/fast-coref"}],"metrics_order":"[\"F1\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":52364,"task":"Coreference Resolution","parent_task":null,"dataset":"OntoNotes","model_name":"G2GT SpanBERT-large reduced","metrics":{"F1":"80.5"},"paper_url":"https://arxiv.org/abs/2203.16574v1","paper_title":"Graph Refinement for Coreference Resolution","paper_date":"2022-03-30","code_links":[{"title":"idiap/g2g-transformer","url":"https://github.com/idiap/g2g-transformer"}],"metrics_order":"[\"F1\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":52365,"task":"Coreference Resolution","parent_task":null,"dataset":"OntoNotes","model_name":"G2GT SpanBERT-large overlap","metrics":{"F1":"80.2"},"paper_url":"https://arxiv.org/abs/2203.16574v1","paper_title":"Graph Refinement for Coreference Resolution","paper_date":"2022-03-30","code_links":[{"title":"idiap/g2g-transformer","url":"https://github.com/idiap/g2g-transformer"}],"metrics_order":"[\"F1\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":52366,"task":"Coreference Resolution","parent_task":null,"dataset":"OntoNotes","model_name":"SpanBERT","metrics":{"F1":"79.6"},"paper_url":"https://arxiv.org/abs/1907.10529v3","paper_title":"SpanBERT: Improving Pre-training by Representing and Predicting Spans","paper_date":"2019-07-24","code_links":[{"title":"facebookresearch/SpanBERT","url":"https://github.com/facebookresearch/SpanBERT"},{"title":"mandarjoshi90/coref","url":"https://github.com/mandarjoshi90/coref"},{"title":"zixinzeng-jennifer/spanbert_trans","url":"https://github.com/zixinzeng-jennifer/spanbert_trans"},{"title":"wooseok-AI/Korean_e2e_CR_BERT","url":"https://github.com/wooseok-AI/Korean_e2e_CR_BERT"},{"title":"amore-upf/masked-coreference","url":"https://github.com/amore-upf/masked-coreference"},{"title":"UnknownGenie/altered-BERT-KPE","url":"https://github.com/UnknownGenie/altered-BERT-KPE"}],"metrics_order":"[\"F1\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":52367,"task":"Coreference Resolution","parent_task":null,"dataset":"OntoNotes","model_name":"U-MEM* + SpanBERT","metrics":{"F1":"79.6"},"paper_url":"https://arxiv.org/abs/2010.02807v3","paper_title":"Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks","paper_date":"2020-10-06","code_links":[{"title":"shtoshni/fast-coref","url":"https://github.com/shtoshni/fast-coref"},{"title":"shtoshni92/fast-coref","url":"https://github.com/shtoshni92/fast-coref"},{"title":"shtoshni92/long-doc-coref","url":"https://github.com/shtoshni92/long-doc-coref"}],"metrics_order":"[\"F1\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":52368,"task":"Coreference Resolution","parent_task":null,"dataset":"OntoNotes","model_name":"longdoc S (ON + PreCo + LitBank + 30k pseudo-singletons)","metrics":{"F1":"79.6"},"paper_url":"https://arxiv.org/abs/2109.09667v1","paper_title":"On Generalization in Coreference Resolution","paper_date":"2021-09-20","code_links":[{"title":"shtoshni/fast-coref","url":"https://github.com/shtoshni/fast-coref"},{"title":"shtoshni92/fast-coref","url":"https://github.com/shtoshni92/fast-coref"}],"metrics_order":"[\"F1\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":52369,"task":"Coreference Resolution","parent_task":null,"dataset":"OntoNotes","model_name":"longdoc S (OntoNotes + PreCo + LitBank)","metrics":{"F1":"79.2"},"paper_url":"https://arxiv.org/abs/2109.09667v1","paper_title":"On Generalization in Coreference Resolution","paper_date":"2021-09-20","code_links":[{"title":"shtoshni/fast-coref","url":"https://github.com/shtoshni/fast-coref"},{"title":"shtoshni92/fast-coref","url":"https://github.com/shtoshni92/fast-coref"}],"metrics_order":"[\"F1\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":52370,"task":"Coreference Resolution","parent_task":null,"dataset":"OntoNotes","model_name":"BERT-large","metrics":{"F1":"76.9"},"paper_url":"https://arxiv.org/abs/1908.09091v4","paper_title":"BERT for Coreference Resolution: Baselines and Analysis","paper_date":"2019-08-24","code_links":[{"title":"mandarjoshi90/coref","url":"https://github.com/mandarjoshi90/coref"},{"title":"wooseok-AI/Korean_e2e_CR_BERT","url":"https://github.com/wooseok-AI/Korean_e2e_CR_BERT"}],"metrics_order":"[\"F1\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":52371,"task":"Coreference Resolution","parent_task":null,"dataset":"OntoNotes","model_name":"BERT + EE","metrics":{"F1":"76.61"},"paper_url":"https://aclanthology.org/P19-1066","paper_title":"Coreference Resolution with Entity Equalization","paper_date":"2019-07-01","code_links":[{"title":"kkjawz/coref-ee","url":"https://github.com/kkjawz/coref-ee"}],"metrics_order":"[\"F1\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":52372,"task":"Coreference Resolution","parent_task":null,"dataset":"OntoNotes","model_name":"BERT-base","metrics":{"F1":"73.9"},"paper_url":"https://arxiv.org/abs/1908.09091v4","paper_title":"BERT for Coreference Resolution: Baselines and Analysis","paper_date":"2019-08-24","code_links":[{"title":"mandarjoshi90/coref","url":"https://github.com/mandarjoshi90/coref"},{"title":"wooseok-AI/Korean_e2e_CR_BERT","url":"https://github.com/wooseok-AI/Korean_e2e_CR_BERT"}],"metrics_order":"[\"F1\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":52373,"task":"Coreference Resolution","parent_task":null,"dataset":"OntoNotes","model_name":"Reinforced + ELMo","metrics":{"F1":"73.8"},"paper_url":"https://aclanthology.org/P19-1064","paper_title":"End-to-end Deep Reinforcement Learning Based Coreference Resolution","paper_date":"2019-07-01","code_links":[],"metrics_order":"[\"F1\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":52374,"task":"Coreference Resolution","parent_task":null,"dataset":"OntoNotes","model_name":"c2f-coref","metrics":{"F1":"73.0"},"paper_url":"http://arxiv.org/abs/1804.05392v1","paper_title":"Higher-order Coreference Resolution with Coarse-to-fine Inference","paper_date":"2018-04-15","code_links":[{"title":"kentonl/e2e-coref","url":"https://github.com/kentonl/e2e-coref"},{"title":"shayneobrien/coreference-resolution","url":"https://github.com/shayneobrien/coreference-resolution"},{"title":"bkntr/coref-ee","url":"https://github.com/bkntr/coref-ee"},{"title":"kkjawz/coref-ee","url":"https://github.com/kkjawz/coref-ee"},{"title":"Filter-Bubble/e2e-Dutch","url":"https://github.com/Filter-Bubble/e2e-Dutch"}],"metrics_order":"[\"F1\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":52375,"task":"Coreference Resolution","parent_task":null,"dataset":"OntoNotes","model_name":"e2e-coref + ELMo + hyperparameter tuning","metrics":{"F1":"72.3"},"paper_url":"http://arxiv.org/abs/1804.05392v1","paper_title":"Higher-order Coreference Resolution with Coarse-to-fine Inference","paper_date":"2018-04-15","code_links":[{"title":"kentonl/e2e-coref","url":"https://github.com/kentonl/e2e-coref"},{"title":"shayneobrien/coreference-resolution","url":"https://github.com/shayneobrien/coreference-resolution"},{"title":"bkntr/coref-ee","url":"https://github.com/bkntr/coref-ee"},{"title":"kkjawz/coref-ee","url":"https://github.com/kkjawz/coref-ee"},{"title":"Filter-Bubble/e2e-Dutch","url":"https://github.com/Filter-Bubble/e2e-Dutch"}],"metrics_order":"[\"F1\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":52376,"task":"Coreference Resolution","parent_task":null,"dataset":"OntoNotes","model_name":"e2e-coref + ELMo","metrics":{"F1":"70.4"},"paper_url":"http://arxiv.org/abs/1802.05365v2","paper_title":"Deep contextualized word representations","paper_date":"2018-02-15","code_links":[{"title":"flairNLP/flair","url":"https://github.com/flairNLP/flair"},{"title":"dmlc/gluon-nlp","url":"https://github.com/dmlc/gluon-nlp"},{"title":"allenai/bilm-tf","url":"https://github.com/allenai/bilm-tf"},{"title":"Hironsan/anago","url":"https://github.com/Hironsan/anago"},{"title":"HIT-SCIR/ELMoForManyLangs","url":"https://github.com/HIT-SCIR/ELMoForManyLangs"},{"title":"UKPLab/elmo-bilstm-cnn-crf","url":"https://github.com/UKPLab/elmo-bilstm-cnn-crf"},{"title":"helboukkouri/character-bert","url":"https://github.com/helboukkouri/character-bert"},{"title":"PrashantRanjan09/WordEmbeddings-Elmo-Fasttext-Word2Vec","url":"https://github.com/PrashantRanjan09/WordEmbeddings-Elmo-Fasttext-Word2Vec"},{"title":"PrashantRanjan09/Elmo-Tutorial","url":"https://github.com/PrashantRanjan09/Elmo-Tutorial"},{"title":"iliaschalkidis/ELMo-keras","url":"https://github.com/iliaschalkidis/ELMo-keras"},{"title":"YC-wind/embedding_study","url":"https://github.com/YC-wind/embedding_study"},{"title":"JHart96/keras_elmo_embedding_layer","url":"https://github.com/JHart96/keras_elmo_embedding_layer"},{"title":"yuanxiaosc/ELMo","url":"https://github.com/yuanxiaosc/ELMo"},{"title":"kaist-dmlab/BioNER","url":"https://github.com/kaist-dmlab/BioNER"},{"title":"zenanz/ChemPatentEmbeddings","url":"https://github.com/zenanz/ChemPatentEmbeddings"},{"title":"AshwinDeshpande96/Hierarchical-Softmax","url":"https://github.com/AshwinDeshpande96/Hierarchical-Softmax"},{"title":"horizonheart/ELMO","url":"https://github.com/horizonheart/ELMO"},{"title":"RundongChou/elmo-chinese-oversimplified","url":"https://github.com/RundongChou/elmo-chinese-oversimplified"},{"title":"griff4692/LMC","url":"https://github.com/griff4692/LMC"},{"title":"bplank/teaching-dl4nlp","url":"https://github.com/bplank/teaching-dl4nlp"},{"title":"IMPLabUniPr/UniParma-at-semeval-2021-task-5","url":"https://github.com/IMPLabUniPr/UniParma-at-semeval-2021-task-5"},{"title":"menajosep/AleatoricSent","url":"https://github.com/menajosep/AleatoricSent"},{"title":"TEAMLAB-Lecture/deep_nlp_101","url":"https://github.com/TEAMLAB-Lecture/deep_nlp_101"},{"title":"bestend/tf2-bi-lstm-crf-nni","url":"https://github.com/bestend/tf2-bi-lstm-crf-nni"},{"title":"yuanjing-zhu/elmo","url":"https://github.com/yuanjing-zhu/elmo"},{"title":"shelleyHLX/bilm_EMLo","url":"https://github.com/shelleyHLX/bilm_EMLo"},{"title":"sarveshsparab/DeepElmoEmbedNer","url":"https://github.com/sarveshsparab/DeepElmoEmbedNer"},{"title":"ankurbanga/Language-Models","url":"https://github.com/ankurbanga/Language-Models"},{"title":"ClaudiaRaffaelli/Protein-subcellular-localization","url":"https://github.com/ClaudiaRaffaelli/Protein-subcellular-localization"},{"title":"mingdachen/bilm-tf","url":"https://github.com/mingdachen/bilm-tf"},{"title":"kafura-kafiri/tf2-elmo","url":"https://github.com/kafura-kafiri/tf2-elmo"},{"title":"kinimod23/NMT_Project","url":"https://github.com/kinimod23/NMT_Project"},{"title":"nlp-research/bilm-tf","url":"https://github.com/nlp-research/bilm-tf"},{"title":"LamLauChiu/Tensorflow_Learning","url":"https://github.com/LamLauChiu/Tensorflow_Learning"},{"title":"cheng18/bilm-tf","url":"https://github.com/cheng18/bilm-tf"},{"title":"seunghwan1228/ELMO","url":"https://github.com/seunghwan1228/ELMO"},{"title":"Mind23-2/MindCode-98","url":"https://github.com/Mind23-2/MindCode-98"},{"title":"young-zonglin/bilm-tf-extended","url":"https://github.com/young-zonglin/bilm-tf-extended"},{"title":"2023-MindSpore-1/ms-code-190","url":"https://github.com/2023-MindSpore-1/ms-code-190"},{"title":"yangzonglin1994/bilm-tf-extended","url":"https://github.com/yangzonglin1994/bilm-tf-extended"},{"title":"Mind23-2/MindCode-135","url":"https://github.com/Mind23-2/MindCode-135"},{"title":"kunde122/bilm-tf","url":"https://github.com/kunde122/bilm-tf"},{"title":"yangrui123/Hidden","url":"https://github.com/yangrui123/Hidden"},{"title":"richinkabra/CoVe-BCN","url":"https://github.com/richinkabra/CoVe-BCN"},{"title":"weixsong/bilm-tf","url":"https://github.com/weixsong/bilm-tf"},{"title":"shaneding/bilm-tf-experimentation","url":"https://github.com/shaneding/bilm-tf-experimentation"}],"metrics_order":"[\"F1\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":52377,"task":"Coreference Resolution","parent_task":null,"dataset":"OntoNotes","model_name":"E2E-CR + ASL","metrics":{"F1":"67.8"},"paper_url":"https://aclanthology.org/D18-1518","paper_title":"Learning Word Representations with Cross-Sentence Dependency for End-to-End Co-reference Resolution","paper_date":"2018-10-01","code_links":[{"title":"luohongyin/coatt-coref","url":"https://github.com/luohongyin/coatt-coref"}],"metrics_order":"[\"F1\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":52378,"task":"Coreference Resolution","parent_task":null,"dataset":"OntoNotes","model_name":"e2e-coref","metrics":{"F1":"67.2"},"paper_url":"http://arxiv.org/abs/1707.07045v2","paper_title":"End-to-end Neural Coreference Resolution","paper_date":"2017-07-21","code_links":[{"title":"kentonl/e2e-coref","url":"https://github.com/kentonl/e2e-coref"},{"title":"shayneobrien/coreference-resolution","url":"https://github.com/shayneobrien/coreference-resolution"},{"title":"ppriyank/Bert-Coref-Resolution-Lee-","url":"https://github.com/ppriyank/Bert-Coref-Resolution-Lee-"},{"title":"Achint08/e2e-coref-keras","url":"https://github.com/Achint08/e2e-coref-keras"}],"metrics_order":"[\"F1\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":52379,"task":"Coreference Resolution","parent_task":null,"dataset":"OntoNotes","model_name":"Reward Rescaling","metrics":{"F1":"65.73"},"paper_url":"http://arxiv.org/abs/1609.08667v3","paper_title":"Deep Reinforcement Learning for Mention-Ranking Coreference Models","paper_date":"2016-09-27","code_links":[{"title":"clarkkev/deep-coref","url":"https://github.com/clarkkev/deep-coref"}],"metrics_order":"[\"F1\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":52380,"task":"Coreference Resolution","parent_task":null,"dataset":"OntoNotes","model_name":"NN Cluster Ranker","metrics":{"F1":"65.29"},"paper_url":"http://arxiv.org/abs/1606.01323v2","paper_title":"Improving Coreference Resolution by Learning Entity-Level Distributed Representations","paper_date":"2016-06-04","code_links":[{"title":"clarkkev/deep-coref","url":"https://github.com/clarkkev/deep-coref"}],"metrics_order":"[\"F1\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":52381,"task":"Coreference Resolution","parent_task":null,"dataset":"OntoNotes","model_name":"Global","metrics":{"F1":"64.21"},"paper_url":"http://arxiv.org/abs/1604.03035v1","paper_title":"Learning Global Features for Coreference Resolution","paper_date":"2016-04-11","code_links":[{"title":"swiseman/nn_coref","url":"https://github.com/swiseman/nn_coref"}],"metrics_order":"[\"F1\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]}]}