{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T18:30:28Z","timestamp":1777573828284,"version":"3.51.4"},"reference-count":74,"publisher":"MIT Press - Journals","license":[{"start":{"date-parts":[[2022,5,17]],"date-time":"2022-05-17T00:00:00Z","timestamp":1652745600000},"content-version":"vor","delay-in-days":136,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,5,16]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Mining an argument structure from text is an important step for tasks such as argument search and summarization. While studies on argument(ation) mining have proposed promising neural network models, they usually suffer from a shortage of training data. To address this issue, we expand the training data with various auxiliary argument mining corpora and propose an end-to-end cross-corpus training method called Multi-Task Argument Mining (MT-AM). To evaluate our approach, we conducted experiments for the main argument mining tasks on several well-established argument mining corpora. The results demonstrate that MT-AM generally outperformed the models trained on a single corpus. Also, the smaller the target corpus was, the better the MT-AM performed. Our extensive analyses suggest that the improvement of MT-AM depends on several factors of transferability among auxiliary and target corpora.<\/jats:p>","DOI":"10.1162\/tacl_a_00481","type":"journal-article","created":{"date-parts":[[2022,5,17]],"date-time":"2022-05-17T16:15:51Z","timestamp":1652804151000},"page":"639-658","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":23,"title":["End-to-end Argument Mining with Cross-corpora Multi-task Learning"],"prefix":"10.1162","volume":"10","author":[{"given":"Gaku","family":"Morio","sequence":"first","affiliation":[{"name":"Research and Development Group, Hitachi, Ltd., Kokubunji, Tokyo, Japan. gaku.morio.vn@hitachi.com"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiroaki","family":"Ozaki","sequence":"additional","affiliation":[{"name":"Research and Development Group, Hitachi, Ltd., Kokubunji, Tokyo, Japan. hiroaki.ozaki.yu@hitachi.com"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Terufumi","family":"Morishita","sequence":"additional","affiliation":[{"name":"Research and Development Group, Hitachi, Ltd., Kokubunji, Tokyo, Japan. terufumi.morishita.wp@hitachi.com"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kohsuke","family":"Yanai","sequence":"additional","affiliation":[{"name":"Research and Development Group, Hitachi, Ltd., Kokubunji, Tokyo, Japan. kohsuke.yanai.cs@hitachi.com"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"281","published-online":{"date-parts":[[2022,5,16]]},"reference":[{"key":"2022051714400153100_bib1","doi-asserted-by":"publisher","first-page":"41","DOI":"10.18653\/v1\/W19-4505","article-title":"Transferring knowledge from discourse to arguments: A case study with scientific abstracts","volume-title":"Proceedings of the 6th Workshop on Argument Mining","author":"Accuosto","year":"2019"},{"key":"2022051714400153100_bib2","doi-asserted-by":"publisher","first-page":"101840","DOI":"10.1016\/j.datak.2020.101840","article-title":"Mining arguments in scientific abstracts with discourse-level embeddings","volume":"129","author":"Accuosto","year":"2020","journal-title":"Data Knowledge Engineering"},{"key":"2022051714400153100_bib3","doi-asserted-by":"publisher","first-page":"pages 2623\u2013pages 2631","DOI":"10.1145\/3292500.3330701","article-title":"Optuna: A next-generation hyperparameter optimization framework","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Akiba","year":"2019"},{"key":"2022051714400153100_bib4","first-page":"6354","article-title":"A neural transition-based model for argumentation mining","volume-title":"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)","author":"Bao","year":"2021"},{"key":"2022051714400153100_bib5","doi-asserted-by":"publisher","first-page":"3615","DOI":"10.18653\/v1\/D19-1371","article-title":"SciBERT: A pretrained language model for scientific text","volume-title":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)","author":"Iz","year":"2019"},{"key":"2022051714400153100_bib6","article-title":"Longformer: The long-document transformer","author":"Iz","year":"2020","journal-title":"arXiv:2004.05150"},{"issue":"3","key":"2022051714400153100_bib7","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1080\/00051144.2020.1761101","article-title":"Structured prediction models for argumentative claim parsing from text","volume":"61","author":"Boltu\u017ei\u0107","year":"2020","journal-title":"Automatika"},{"key":"2022051714400153100_bib8","doi-asserted-by":"crossref","DOI":"10.3115\/1118078.1118083","article-title":"Building a discourse-tagged corpus in the framework of Rhetorical Structure Theory","volume-title":"Proceedings of the Second SIGdial Workshop on Discourse and Dialogue","author":"Carlson","year":"2001"},{"key":"2022051714400153100_bib9","first-page":"558","article-title":"IMHO fine-tuning improves claim detection","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)","author":"Chakrabarty","year":"2019"},{"key":"2022051714400153100_bib10","first-page":"1396","article-title":"On the shortest arborescence of a directed graph","volume":"14","author":"Chu","year":"1965","journal-title":"Science Sinica"},{"key":"2022051714400153100_bib11","first-page":"45","article-title":"Dataset independent baselines for relation prediction in argument mining","volume-title":"Frontiers in Artificial Intelligence and Applications","author":"Cocarascu","year":"2020"},{"key":"2022051714400153100_bib12","first-page":"95","article-title":"A fundamental algorithm for dependency parsing","volume-title":"In Proceedings of the 39th Annual ACM Southeast Conference","author":"Covington","year":"2001"},{"key":"2022051714400153100_bib13","doi-asserted-by":"publisher","first-page":"2055","DOI":"10.18653\/v1\/D17-1218","article-title":"What is the essence of a claim? Cross-domain claim identification","volume-title":"Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing","author":"Daxenberger","year":"2017"},{"key":"2022051714400153100_bib14","first-page":"4171","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)","author":"Devlin","year":"2019"},{"key":"2022051714400153100_bib15","article-title":"Deep biaffine attention for neural dependency parsing","volume-title":"5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings","author":"Dozat","year":"2017"},{"key":"2022051714400153100_bib16","doi-asserted-by":"publisher","first-page":"484","DOI":"10.18653\/v1\/P18-2077","article-title":"Simpler but more accurate semantic dependency parsing","volume-title":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)","author":"Dozat","year":"2018"},{"key":"2022051714400153100_bib17","first-page":"2006","article-title":"Span-based joint entity and relation extraction with transformer pre-training","volume-title":"Frontiers in Artificial Intelligence and Applications","author":"Eberts","year":"2020"},{"key":"2022051714400153100_bib18","doi-asserted-by":"crossref","first-page":"2236","DOI":"10.18653\/v1\/D15-1267","article-title":"On the role of discourse markers for discriminating claims and premises in argumentative discourse","volume-title":"Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing","author":"Eckle-Kohler","year":"2015"},{"key":"2022051714400153100_bib19","doi-asserted-by":"publisher","DOI":"10.2307\/358423","volume-title":"Fundamentals of Argumentation Theory a Handbook of Historical Backgrounds and Contemporary Developments","author":"Eemeren","year":"1996"},{"key":"2022051714400153100_bib20","doi-asserted-by":"publisher","first-page":"11","DOI":"10.18653\/v1\/P17-1002","article-title":"Neural end-to-end learning for computational argumentation mining","volume-title":"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"Eger","year":"2017"},{"key":"2022051714400153100_bib21","doi-asserted-by":"publisher","first-page":"25","DOI":"10.18653\/v1\/2020.iwpt-1.4","article-title":"Integrating graph-based and transition-based dependency parsers in the deep contextualized era","volume-title":"Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies","author":"Falenska","year":"2020"},{"key":"2022051714400153100_bib22","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-007-0357-5","volume-title":"Argument Structure: Representation and Theory","author":"Freeman","year":"2011"},{"key":"2022051714400153100_bib23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18653\/v1\/W18-5201","article-title":"Argumentative link prediction using residual networks and multi-objective learning","volume-title":"Proceedings of the 5th Workshop on Argument Mining","author":"Galassi","year":"2018"},{"key":"2022051714400153100_bib24","article-title":"Multi-task attentive residual networks for argument mining","volume":"abs\/2102.12227","author":"Galassi","year":"2021","journal-title":"CoRR"},{"key":"2022051714400153100_bib25","doi-asserted-by":"crossref","first-page":"2","DOI":"10.3115\/v1\/D14-1002","article-title":"Modeling interestingness with deep neural networks","volume-title":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)","author":"Gao","year":"2014"},{"issue":"5","key":"2022051714400153100_bib26","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1016\/j.neunet.2005.06.042","article-title":"Framewise phoneme classification with bidirectional LSTM and other neural network architectures","volume":"18","author":"Graves","year":"2005","journal-title":"Neural Networks"},{"key":"2022051714400153100_bib27","doi-asserted-by":"crossref","first-page":"3794","DOI":"10.18653\/v1\/2020.coling-main.337","article-title":"Unleashing the power of neural discourse parsers - a context and structure aware approach using large scale pretraining","volume-title":"Proceedings of the 28th International Conference on Computational Linguistics","author":"Guz","year":"2020"},{"issue":"1","key":"2022051714400153100_bib28","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1162\/COLI_a_00276","article-title":"Argumentation mining in user-generated web discourse","volume":"43","author":"Habernal","year":"2017","journal-title":"Computational Linguistics"},{"key":"2022051714400153100_bib29","doi-asserted-by":"publisher","first-page":"328","DOI":"10.18653\/v1\/P18-1031","article-title":"Universal language model fine-tuning for text classification","volume-title":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"Howard","year":"2018"},{"key":"2022051714400153100_bib30","doi-asserted-by":"publisher","first-page":"2306","DOI":"10.18653\/v1\/D19-1235","article-title":"Predicting discourse structure using distant supervision from sentiment","volume-title":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)","author":"Huber","year":"2019"},{"key":"2022051714400153100_bib31","doi-asserted-by":"crossref","first-page":"13","DOI":"10.3115\/v1\/P14-1002","article-title":"Representation learning for text-level discourse parsing","volume-title":"Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"Ji","year":"2014"},{"key":"2022051714400153100_bib32","doi-asserted-by":"publisher","first-page":"2120","DOI":"10.18653\/v1\/2020.acl-main.192","article-title":"Generalizing natural language analysis through span-relation representations","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","author":"Jiang","year":"2020"},{"key":"2022051714400153100_bib33","article-title":"Adam: A method for stochastic optimization","volume-title":"Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015","author":"Kingma","year":"2015"},{"key":"2022051714400153100_bib34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3115\/v1\/W15-0501","article-title":"Linking the thoughts: Analysis of argumentation structures in scientific publications","volume-title":"Proceedings of the 2nd Workshop on Argumentation Mining","author":"Kirschner","year":"2015"},{"key":"2022051714400153100_bib35","doi-asserted-by":"publisher","first-page":"4691","DOI":"10.18653\/v1\/P19-1464","article-title":"An empirical study of span representations in argumentation structure parsing","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","author":"Kuribayashi","year":"2019"},{"key":"2022051714400153100_bib36","doi-asserted-by":"publisher","first-page":"40","DOI":"10.18653\/v1\/W18-5206","article-title":"An argument-annotated corpus of scientific publications","volume-title":"Proceedings of the 5th Workshop on Argument Mining","author":"Lauscher","year":"2018"},{"issue":"4","key":"2022051714400153100_bib37","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1162\/coli_a_00364","article-title":"Argument mining: A survey","volume":"45","author":"Lawrence","year":"2019","journal-title":"Computational Linguistics"},{"key":"2022051714400153100_bib38","doi-asserted-by":"crossref","first-page":"4487","DOI":"10.18653\/v1\/P19-1441","article-title":"Multi-task deep neural networks for natural language understanding","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","author":"Liu","year":"2019"},{"key":"2022051714400153100_bib39","article-title":"RoBERTa: A robustly optimized BERT pretraining approach","author":"Liu","year":"2019","journal-title":"arXiv preprint arXiv:1907.11692"},{"key":"2022051714400153100_bib40","first-page":"2108","article-title":"Transformer-based argument mining for healthcare applications","volume-title":"Frontiers in Artificial Intelligence and Applications","author":"Mayer","year":"2020"},{"key":"2022051714400153100_bib41","doi-asserted-by":"publisher","first-page":"1105","DOI":"10.18653\/v1\/P16-1105","article-title":"End-to-end relation extraction using LSTMs on sequences and tree structures","volume-title":"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"Miwa","year":"2016"},{"key":"2022051714400153100_bib42","doi-asserted-by":"publisher","first-page":"3259","DOI":"10.18653\/v1\/2020.acl-main.298","article-title":"Towards better non-tree argument mining: Proposition-level biaffine parsing with task-specific parameterization","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","author":"Morio","year":"2020"},{"key":"2022051714400153100_bib43","doi-asserted-by":"publisher","first-page":"791","DOI":"10.18653\/v1\/2020.semeval-1.101","article-title":"Hitachi at SemEval-2020 task 7: Stacking at scale with heterogeneous language models for humor recognition","volume-title":"Proceedings of the Fourteenth Workshop on Semantic Evaluation","author":"Morishita","year":"2020"},{"key":"2022051714400153100_bib44","doi-asserted-by":"publisher","first-page":"985","DOI":"10.18653\/v1\/P17-1091","article-title":"Argument mining with structured SVMs and RNNs","volume-title":"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"Niculae","year":"2017"},{"key":"2022051714400153100_bib45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18653\/v1\/2020.conll-shared.1","article-title":"MRP 2020: The second shared task on cross-framework and cross-lingual meaning representation parsing","volume-title":"Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing","author":"Oepen","year":"2020"},{"key":"2022051714400153100_bib46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18653\/v1\/K19-2001","article-title":"MRP 2019: Cross-framework meaning representation parsing","volume-title":"Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning","author":"Oepen","year":"2019"},{"key":"2022051714400153100_bib47","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1145\/2746090.2746118","article-title":"Toward machine-assisted participation in erulemaking: An argumentation model of evaluability","volume-title":"Proceedings of the 15th International Conference on Artificial Intelligence and Law","author":"Park","year":"2015"},{"key":"2022051714400153100_bib48","article-title":"A corpus of eRulemaking user comments for measuring evaluability of arguments","volume-title":"Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)","author":"Park","year":"2018"},{"key":"2022051714400153100_bib49","doi-asserted-by":"publisher","first-page":"39","DOI":"10.3115\/v1\/W15-0506","article-title":"Conditional random fields for identifying appropriate types of support for propositions in online user comments","volume-title":"Proceedings of the 2nd Workshop on Argumentation Mining","author":"Park","year":"2015"},{"key":"2022051714400153100_bib50","doi-asserted-by":"publisher","first-page":"88","DOI":"10.3115\/v1\/W14-2112","article-title":"Towards segment-based recognition of argumentation structure in short texts","volume-title":"Proceedings of the First Workshop on Argumentation Mining","author":"Peldszus","year":"2014"},{"key":"2022051714400153100_bib51","doi-asserted-by":"publisher","first-page":"938","DOI":"10.18653\/v1\/D15-1110","article-title":"Joint prediction in MST-style discourse parsing for argumentation mining","volume-title":"Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing","author":"Peldszus","year":"2015"},{"key":"2022051714400153100_bib52","first-page":"801","article-title":"An annotated corpus of argumentative microtexts","volume-title":"Argumentation and Reasoned Action: Proceedings of the 1st European Conference on Argumentation, Lisbon 2015 \/ Vol. 2","author":"Peldszus","year":"2016"},{"key":"2022051714400153100_bib53","doi-asserted-by":"publisher","first-page":"1384","DOI":"10.18653\/v1\/N16-1164","article-title":"End-to-end argumentation mining in student essays","volume-title":"Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"Persing","year":"2016"},{"key":"2022051714400153100_bib54","doi-asserted-by":"publisher","first-page":"1364","DOI":"10.18653\/v1\/D17-1143","article-title":"Here\u2019s my point: Joint pointer architecture for argument mining","volume-title":"Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing","author":"Potash","year":"2017"},{"key":"2022051714400153100_bib55","doi-asserted-by":"crossref","first-page":"12","DOI":"10.18653\/v1\/2021.argmining-1.2","article-title":"Multi-task and multi-corpora training strategies to enhance argumentative sentence linking performance","volume-title":"Proceedings of the 8th Workshop on Argument Mining","author":"Putra","year":"2021"},{"key":"2022051714400153100_bib56","first-page":"97","article-title":"Parsing argumentative structure in English-as-foreign-language essays","volume-title":"Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications","author":"Putra","year":"2021"},{"key":"2022051714400153100_bib57","doi-asserted-by":"publisher","first-page":"440","DOI":"10.18653\/v1\/D15-1050","article-title":"Show me your evidence - an automatic method for context dependent evidence detection","volume-title":"Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing","author":"Rinott","year":"2015"},{"key":"2022051714400153100_bib58","doi-asserted-by":"publisher","first-page":"35","DOI":"10.18653\/v1\/N18-2006","article-title":"Multi-task learning for argumentation mining in low-resource settings","volume-title":"Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)","author":"Schulz","year":"2018"},{"key":"2022051714400153100_bib59","doi-asserted-by":"publisher","first-page":"231","DOI":"10.18653\/v1\/P16-2038","article-title":"Deep multi-task learning with low level tasks supervised at lower layers","volume-title":"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)","author":"S\u00f8gaard","year":"2016"},{"issue":"56","key":"2022051714400153100_bib60","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"Journal of Machine Learning Research"},{"key":"2022051714400153100_bib61","doi-asserted-by":"publisher","first-page":"46","DOI":"10.3115\/v1\/D14-1006","article-title":"Identifying argumentative discourse structures in persuasive essays","volume-title":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)","author":"Stab","year":"2014"},{"issue":"3","key":"2022051714400153100_bib62","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1162\/COLI_a_00295","article-title":"Parsing argumentation structures in persuasive essays","volume":"43","author":"Stab","year":"2017","journal-title":"Computational Linguistics"},{"key":"2022051714400153100_bib63","doi-asserted-by":"publisher","DOI":"10.2200\/S00883ED1V01Y201811HLT040","volume-title":"Argumentation Mining","author":"Stede","year":"2018"},{"key":"2022051714400153100_bib64","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511840005","volume-title":"The Uses of Argument","author":"Toulmin","year":"2003","edition":"second"},{"key":"2022051714400153100_bib65","first-page":"41","article-title":"Aspect-based argument mining","volume-title":"Proceedings of the 7th Workshop on Argument Mining","author":"Trautmann","year":"2020"},{"issue":"05","key":"2022051714400153100_bib66","doi-asserted-by":"publisher","first-page":"9048","DOI":"10.1609\/aaai.v34i05.6438","article-title":"Fine-grained argument unit recognition and classification","volume":"34","author":"Trautmann","year":"2020","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"2022051714400153100_bib67","doi-asserted-by":"publisher","first-page":"176","DOI":"10.18653\/v1\/2021.eacl-demos.22","article-title":"Massive choice, ample tasks (MaChAmp): A toolkit for multi-task learning in NLP","volume-title":"Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations","author":"Goot","year":"2021"},{"key":"2022051714400153100_bib68","first-page":"5998","volume-title":"Advances in Neural Information Processing Systems 30","author":"Vaswani","year":"2017"},{"key":"2022051714400153100_bib69","doi-asserted-by":"publisher","first-page":"142","DOI":"10.18653\/v1\/N18-2023","article-title":"A transition-based algorithm for unrestricted AMR parsing","volume-title":"Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)","author":"Vilares","year":"2018"},{"key":"2022051714400153100_bib70","first-page":"2692","article-title":"Pointer networks","volume-title":"Advances in Neural Information Processing Systems","author":"Vinyals","year":"2015"},{"key":"2022051714400153100_bib71","doi-asserted-by":"publisher","first-page":"2379","DOI":"10.18653\/v1\/D17-1253","article-title":"The impact of modeling overall argumentation with tree kernels","volume-title":"Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing","author":"Wachsmuth","year":"2017"},{"key":"2022051714400153100_bib72","doi-asserted-by":"publisher","first-page":"5480","DOI":"10.18653\/v1\/2020.coling-main.478","article-title":"Argumentation mining on essays at multi scales","volume-title":"Proceedings of the 28th International Conference on Computational Linguistics","author":"Wang","year":"2020"},{"key":"2022051714400153100_bib73","doi-asserted-by":"publisher","first-page":"444","DOI":"10.18653\/v1\/P18-2071","article-title":"SciDTB: Discourse dependency TreeBank for scientific abstracts","volume-title":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)","author":"An","year":"2018"},{"key":"2022051714400153100_bib74","first-page":"669","article-title":"End-to-end argument mining as biaffine dependency parsing","volume-title":"Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume","author":"Ye","year":"2021"}],"container-title":["Transactions of the Association for Computational Linguistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/direct.mit.edu\/tacl\/article-pdf\/doi\/10.1162\/tacl_a_00481\/2022965\/tacl_a_00481.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/direct.mit.edu\/tacl\/article-pdf\/doi\/10.1162\/tacl_a_00481\/2022965\/tacl_a_00481.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,17]],"date-time":"2022-05-17T16:16:27Z","timestamp":1652804187000},"score":1,"resource":{"primary":{"URL":"https:\/\/direct.mit.edu\/tacl\/article\/doi\/10.1162\/tacl_a_00481\/111222\/End-to-end-Argument-Mining-with-Cross-corpora"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":74,"URL":"https:\/\/doi.org\/10.1162\/tacl_a_00481","relation":{},"ISSN":["2307-387X"],"issn-type":[{"value":"2307-387X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022]]},"published":{"date-parts":[[2022]]}}}