Abstract
Argumentation mining is a natural language understanding task consisting of several subtasks: relevance detection, stance classification, argument quality assessment and fact checking. In this work we propose several architectures for the analysis of argumentative texts based on BERT. We also show, that models, which jointly learn argumentation mining subtasks outperform pipelines of models trained on a single tasks. Additionally we explore transfer learning approach based on pretraining for the natural language inference task, which achieves highest score on tasks of argumentation mining among the models trained on english corpora.
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References
Aharoni, E., et al.: A benchmark dataset for automatic detection of claims and evidence in the context of controversial topics. In: Proceedings of the First Workshop on Argumentation Mining, pp. 64–68 (2014)
Al-Khatib, K., Wachsmuth, H., Hagen, M., Stein, B., Köhler, J.: Webis-debate-16. Zenodo, January 2016. https://doi.org/10.5281/zenodo.3251804
Barrón-Cedeño, A., et al.: Overview of CheckThat! 2020: automatic identification and verification of claims in social media. In: Arampatzis, A., et al. (eds.) CLEF 2020. LNCS, vol. 12260, pp. 215–236. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58219-7_17
Bowman, S.R., Angeli, G., Potts, C., Manning, C.D.: A large annotated corpus for learning natural language inference. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics (2015)
Chernodub, A., et al.: TARGER: neural argument mining at your fingertips. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 195–200 (2019)
Cui, L., Cheng, S., Wu, Y., Zhang, Y.: Does BERT solve commonsense task via commonsense knowledge? arXiv preprint arXiv:2008.03945 (2020)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Ein-Dor, L., et al.: Corpus wide argument mining-a working solution. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 7683–7691 (2020)
Gleize, M., et al.: Are you convinced? Choosing the more convincing evidence with a Siamese network. arXiv preprint arXiv:1907.08971 (2019)
Gretz, S., et al.: A large-scale dataset for argument quality ranking: construction and analysis. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 7805–7813 (2020)
Levy, R., Bogin, B., Gretz, S., Aharonov, R., Slonim, N.: Towards an argumentative content search engine using weak supervision. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 2066–2081 (2018)
Lippi, M., Torroni, P.: MARGOT: a web server for argumentation mining. Expert Syst. Appl. 65, 292–303 (2016)
Reimers, N., Gurevych, I.: Sentence-BERT: sentence embeddings using Siamese BERT-networks. arXiv preprint arXiv:1908.10084 (2019)
Shnarch, E., et al.: Will it blend? Blending weak and strong labeled data in a neural network for argumentation mining. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 599–605 (2018)
Stab, C., Miller, T., Gurevych, I.: Cross-topic argument mining from heterogeneous sources using attention-based neural networks. arXiv preprint arXiv:1802.05758 (2018)
Sun, Z., et al.: Self-explaining structures improve NLP models. arXiv preprint arXiv:2012.01786 (2020)
Thorne, J., Vlachos, A., Christodoulopoulos, C., Mittal, A.: FEVER: a large-scale dataset for fact extraction and verification. arXiv preprint arXiv:1803.05355 (2018)
Toledo, A., et al.: Automatic argument quality assessment-new datasets and methods. arXiv preprint arXiv:1909.01007 (2019)
Toledo-Ronen, O., Orbach, M., Bilu, Y., Spector, A., Slonim, N.: Multilingual argument mining: datasets and analysis. arXiv preprint arXiv:2010.06432 (2020)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
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The study is supported by Russian Science Foundation, project 21-71-30003.
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Dimov, I., Dobrov, B. (2022). Methods for Automatic Argumentation Structure Prediction. In: Pozanenko, A., Stupnikov, S., Thalheim, B., Mendez, E., Kiselyova, N. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2021. Communications in Computer and Information Science, vol 1620. Springer, Cham. https://doi.org/10.1007/978-3-031-12285-9_14
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