[1] Moens M F, Boiy E, Palau R M, et al. Automatic detection of arguments in legal texts[C]//Proceedings of the 11th International Conference on Artificial Intelligence and Law, 2007: 225-230.
[2] Pang B, Lee L, Vaithyanathan S. Thumbs up? sentiment classification using machine learning techniques[C]//Proceedings of the EMNLP, 2002: 79-86.
[3] Cabrio E, Villata S. Five years of argument mining: A data-driven analysis[C]//Proceedings of the 27th IJCAI, 2018, 18: 5427-5433.
[4] Green N, Ashley K D, Litman D, et al. The first workshop on argumentation mining[EB/OL]. https://www.uncg.edu/cmp/ArgMining 2014/. [2021-08-02].
[5] Al-Khatib. The 8th Workshop on Argument Mining, 2021[EB/OL]. https: //2021.argmining.org/index.html [2021-08-02].
[6] Simon P. Computational models of argument: Proceedings of COMMA 2014[M]. Amsterdam: Ios Press, 2014.
[7] Huang Z, Xu W, Yu K. Bidirectional LSTM-CRF models for sequence tagging[J]. arXiv preprint arXiv: 1508.01991, 2015.
[8] Sutskever I, Vinyals O, Le Q V. Sequence to sequence learning with neural networks[C]//Proceedings of the NIPS, 2014: 3104-3112.
[9] Chopra S, Auli M, Rush A M. Abstractive sentence summarization with attentive recurrent neural networks[C]//Proceedings of the NAACL, 2016: 93-98.
[10] Lawrence J, Reed C. Argument mining: A survey[J]. Computational Linguistics, 2020, 45(4): 765-818.
[11] Vecchi E M, Falk N, Jundi I, et al. Towards argument mining for social good: A survey[C]//Proceedings of the 59th ACL, 2021: 1338-1352.
[12] 李永泽, 欧石燕. 论辩挖掘研究综述[J]. 图书情报工作, 2020, 64(19): 128-139.
[13] Peldszus A, Stede M. From argument diagrams to argumentation mining in texts: a survey[J]. IJCINI, 2013, 7(1): 1-31.
[14] Habernal I, Gurevych I. Argumentation mining in user-generated web discourse[J]. Computational Linguistics, 2017, 43(1): 125-179.
[15] Park J, Cardie C. A corpus of erulemaking user comments for measuring evaluability of arguments[C]//Proceedings of the LREC, 2018: 1623-1628
[16] Swanson R, Ecker B, Walker M. Argument mining: Extracting arguments from online dialogue[C]//Proceedings of the 16th SIGDIAL, 2015: 217-226.
[17] Lauscher A, Glava G, et al. Investigating the role of argumentation in the rhetorical analysis of scientific publicationswith neural multi-task learning models[C]//Proceedings of the EMNLP, 2018: 3326-3338.
[18] Gemechu D, Reed C. Decompositional argument mining: a general purpose approach for argument graph construction[C]//Proceedings of the 57th ACL, 2019: 516-526.
[19] Haddadan S, Cabrio E, Villata S. Yes, we can! mining arguments in 50 years of us presidential campaign debates[C]//Proceedings of the 57th ACL, 2019: 4684-4690.
[20] Lippi M, Torroni P. Argument mining from speech: detecting claims in political debates[C]//Proceedings of the 30th AAAI Conference on Artificial Intelligence, 2016.
[21] Menini S, Cabrio E, Tonelli S, et al. Never retreat, never retract: Argumentation analysis for political speeches[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence, 2018.
[22] Cabrio E, Villata S. A natural language bipolar argumentation approach to support users in online debate interactions[J]. Argument & Computation, 2013, 4(3): 209-230.
[23] Al Khatib K, Wachsmuth H, Hagen M, et al. Cross-domain mining of argumentative text through distant supervision[C]//Proceedings of the NAACL, 2016: 1395-1404.
[24] Mochales R, Moens M F. Argumentation mining[J]. Artificial Intelligence and Law, 2011, 19(1): 1-22.
[25] Poudyal P, avelka J, Ieven A, et al. Echr: legal corpus for argument mining[C]//Proceedings of the 7th Workshop on Argument Mining, 2020: 67-75.
[26] Stab C, Gurevych I. Parsing argumentation structures in persuasive essays[J]. Computational Linguistics, 2017, 43(3): 619-659.
[27] Song W, Song Z, et al. Discourse self-attention for discourse element identification in argumentative student essays[C]//Proceedings of the EMNLP, 2020: 2820-2830.
[28] Mayer T, Cabrio E, Lippi M, et al. Argument mining on clinical trials[C]//Proceedings of the 7th COMMA, 2018: 137-148.
[29] Mayer T, Cabrio E, Villata S. Transformer-based argument mining for healthcare applications[C]//Proceedings of the 24th ECAI, 2020: 2108-2115.
[30] Fromm M, Faerman E, Berrendorf M, et al. Argument mining driven analysis of peer-reviews[C]//Proceedings of the 35th AAAI, 2021: 35(6).
[31] Gupta S, Singh P, Sundriyal M, et al. LESA: Linguistic encapsulation and semantic amalgamation based generalised claim detection from online content[C]//Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, 2021: 3178-3188.
[32] Schaefer R, Stede M. Annotation and detection of arguments in tweets[C]//Proceedings of the 7th Workshop on Argument Mining, 2020: 53-58.
[33] Eger S, Daxenberger J, Stab C, et al. Cross-lingual argumentation mining: Machine translation (and a bit of projection) is all you need![C]//Proceedings of the 27th COLING, 2018: 831-844.
[34] Toledo-Ronen O, Orbach M, Bilu Y, et al. Multilingual argument mining: Datasets and analysis[C]//Proceedings of the EMNLP Findings, 2020: 303-317.
[35] Daxenberger J, Eger S, Habernal I, et al. What is the essence of a claim? cross-domain claim identification[C]//Proceedings of the EMNLP, 2017: 2055-2066.
[36] Jakobsen T S T, Barrett M, Sgaard A. Spurious correlations in cross-topic argument mining[C]//Proceedings of the 10th STARSEM, 2021: 263-277.
[37] Ji L, Wei Z, Li J, et al. Discrete argument representation learning for interactive argument pair identification[C]//Proceedings of the NAACL, 2021: 5467-5478.
[38] Cheng L, Bing L, Yu Q, et al. Argument pair extraction from peer review and rebuttal via multi-task learning[C]//Proceedings of the EMNLP. 2020: 7000-7011.
[39] Habernal I, Gurevych I. Which argument is more convincing? analyzing and predicting convincingness of web arguments using bidirectionalLSTM[C]//Proceedings of the 54th ACL, 2016: 1589-1599.
[40] Gienapp L, Stein B, Hagen M, et al. Efficient pairwise annotation of argument quality[C]//Proceedings of the 58th ACL, 2020: 5772-5781.
[41] Levy R, Bilu Y, et al. Context dependent claim detection[C]//Proceedings of the COLING, 2014: 1489-1500.
[42] Gleize M, Shnarch E, et al. Are you convinced? Choosing the more convincing evidence with a siamese network[C]//Proceedings of the 57th ACL. 2019: 967-976.
[43] Yova K. IBM proeject debater[EB/OL]. https: //www.research.ibm.com/interactive/project-debater/. [2021-08-02].
[44] Ein-Dor L, Shnarch E, Dankin L, et al. Corpus wide argument mining-a working solution[C]//Proceedings of the 34th AAAI, 2020.
[45] Wachsmuth H, Syed S, Stein B. Retrieval of the best counterargument without prior topic knowledge[C]//Proceedings of the 56th ACL, 2018: 241-251.
[46] Wang L, Ling W. Neural network-based abstract generation for opinions and arguments[C]//Proceedings of the NAACL, 2016: 47-57.
[47] Zaremba W, Sutskever I, Vinyals O. Recurrent neural network regularization[J]. arXiv preprint arXiv: 1409.2329, 2014.
[48] Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural computation, 1997, 9(8): 1735-1780.
[49] Accuosto P, Saggion H. Mining arguments in scientific abstracts with discourse-level embeddings[J]. Data & Knowledge Engineering, 2020, 129: 101840.
[50] Guggilla C, Miller T, Gurevych I. CNN-and LSTM-based claim classification in online user comments[C]//Proceedings of the 26th COLING, 2016: 2740-2751.
[51] Cocarascu O, Toni F. Identifying attack and support argumentative relations using deep learning[C]//Proceedings of the EMNLP, 2017: 1374-1379.
[52] Eger S, Daxenberger J, Gurevych I. Neural end-to-end learning for computational argumentation mining[C]//Proceedings of the 55th ACL, 2017: 11-22.
[53] Morio G, Ozaki H, Morishita T, et al. Towards better non-tree argument mining: proposition-level biaffine parsing with task-specific parameterization[C]//Proceedings of the 58th ACL, 2020: 3259-3266.
[54] Li Y, Tarlow D, et al. Gated graph sequence neural networks[J]. arXiv preprint arXiv: 1511.05493, 2015.
[55] Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks[J]. arXiv preprint arXiv: 1609.02907, 2016.
[56] Yuan J, Wei Z, Zhao D, et al. Leveraging argumentation knowledge graph for interactive argument pair identification[C]//Proceedings of the 59th ACL, 2021: 5467-5478.
[57] Song L, Gildea D, Zhang Y, et al. Semantic neural machine translation using AMR[J]. Transactions of the Association for Computational Linguistics, 2019, 7: 19-31.
[58] Qiu D, Zhang Y, Feng X, et al. Machine reading comprehension using structural knowledge graph-aware network[C]//Proceedings of the EMNLP-IJCNLP, 2019: 5896-5901.
[59] Devlin J, Chang M W, et al. BERT: Pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the NAACL, 2019: 4171-4186.
[60] Reimers N, Schiller B, Beck T, et al. Classification and clustering of arguments with contextualized word embeddings[C]//Proceedings of the 57th ACL, 2019: 567-578.
[61] Vaswani A, Shazeer N, et al. Attention is all you need[C]//Proceedings of the 31st NeurIPS. 2017: 5998-6008.
[62] Paul D, Opitz J, Becker M, et al. Argumentative relation classification with background knowledge[J]. Computational Models of Argument, 2020: 319-330.
[63] Hua X, Nikolov M, Badugu N, et al. Argument mining for understanding peer reviews[C]//Proceedings of the NAACL, 2019: 2131-2137.
[64] Li M, Gao Y, Wen H, et al. Joint RNN model for argument component boundary detection[C]//Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. IEEE, 2017: 57-62.
[65] Wührl A, Klinger R. Claim detection in biomedical twitter posts[C]//Proceedings of the 20th Workshop on Biomedical Language Processing,2021: 131-142.
[66] Wang H, Huang Z, Dou Y, et al. Argumentation mining on essays at multi scales[C]//Proceedings of the 28th COLING, 2020: 5480-5493.
[67] Mayer T, Marro S, Cabrio E, et al. Generating adversarial examples for topic-dependent argument classification[J]. Computational Models of Argument. IOS Press, 2020: 33-44.
[68] Dagan I, Glickman O. Probabilistic textual entailment: generic applied modeling of language variability[J]. Learning Methods for Text Understanding and Mining, 2004: 26-29.
[69] Opitz J. Argumentative relation classification as plausibility ranking[J]. arXiv preprint arXiv: 1909.09031, 2019.
[70] Hou R, Chang H, Ma B, et al. Cross attention network for few-shot classification[C]//Proceedings of the 33rd NeurIPS, 2019: 4003-4014.
[71] Chakrabarty T, Hidey C, et al. AMPERSAND: Argument mining for persuasive online discussions[C]//Proceedings of the EMNLP, 2019: 2933-2943.
[72] Ji Y, Eisenstein J. Representation learning for text-level discourse parsing[C]//Proceedings of the 52nd ACL, 2014: 13-24.
[73] Miwa M, Bansal M. End-to-end relation extraction using LSTMs on sequences and tree structures[C]//Proceedings of the 54th ACL, 2016: 1105-1116.
[74] Potash P, Romanov A, Rumshisky A. Heres my point: joint pointer architecture for argument mining[C]//Proceedings of the EMNLP, 2017: 1364-1373.
[75] See A, Liu P J, Manning C D. Get to the point: Summarization with pointer-generator networks[C]//Proceedings of the 55th ACL, 2017: 1073-1083.
[76] Kuribayashi T, Ouchi H, Inoue N, et al. An empirical study of span representations in argumentation structure parsing[C]//Proceedings of the 57th ACL, 2019: 4691-4698.
[77] Wang W, Chang B. Graph-based dependency parsing with bidirectional LSTM[C]//Proceedings of the 54th ACL, 2016: 2306-2315.
[78] Niculae V, Park J, Cardie C. Argument mining with structured SVMs and RNNs[C]//Proceedings of the 55th ACL, 2017: 985-995.
[79] Dozat T, Manning C D. Deep biaffine attention for neural dependency parsing[J]. arXiv preprint arXiv: 1611.01734, 2016.
[80] Ye Y, Teufel S. End-to-end argument mining as biaffine dependency parsing[C]//Proceedings of the 16th EACL, 2021: 669-678.
[81] Bao J, Fan C, Wu J, et al. A neural transition-based model for argumentation mining[C]//Proceedings of the 59th ACL, 2021: 6354-6364.
[82] Zhang Y, Nivre J. Transition-based dependency parsing with rich non-local features[C]//Proceedings of the 49th ACL: Human Language Technologies, 2011: 188-193.
[83] Hori T, Nakamura A. Speech recognition algorithms using weighted finite-state transducers[J]. Synthesis Lectures on Speech and Audio Processing, 2013, 9(1): 1-162.
[84] Rolfe J T. Discretevariational autoencoders[J]. arXiv preprint arXiv: 1609.02200, 2016.[85] Chung J, Gulcehre C, Cho K H, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[J]. arXiv preprint arXiv: 1412.3555, 2014.
[86] Cheng L, Wu T, Bing L, et al. Argument pair extraction via attention-guided multi-layer multi-cross encoding[C]//Proceedings of the 59th ACL, 2021: 6341-6356.
[87] Ji L, Wei Z, Hu X, et al. Incorporating argument-level interactions for persuasion comments evaluation using co-attention model[C]//Proceedings of the COLING, 2018: 3703-3714.
[88] Huang K Y, Huang H H, Chen H H. HARGAN: heterogeneous argument attention network for persuasiveness prediction[C]//Proceedings of the 35th AAAI, 2021.
[89] Wang X, Ji H, et al. Heterogeneous graph attention network[C]//Proceedings of the WWW, 2019: 2022-2032.
[90] Zhao X, Durmus E, Zhang H, et al. Leveraging topic relatedness for argument persuasion[C]//Proceedings of the 59th ACL, 2021: 4401-4407.
[91] Brown T B, Mann B, Ryder N, et al. Language models are few-shot learners[J]. arXiv preprint arXiv: 2005.14165, 2020.