Abstract
Argument Pair Extraction (APE) is an extension of argument mining that focuses on identifying argument pairs from two passages that have an intrinsic interaction, such as peer review and rebuttal. Existing studies have divided this task into separate subtasks for argument mining and sentence relation classification, but they overlook the connection between the two subtasks, leading to the accumulation of errors in argument pair extraction. To address this issue, we propose the Multi-fusion Cross-update Recurrent Network (MCRN), which includes two cross-updated units: an argument mining unit and a sentence pairing unit. Specifically, we cross-update the sentence representations of both units to learn the interaction between them, allowing the acquired sentence features to contain both argumentation and sentence relation information. We also designed a recurrent structure to iteratively learn these two units, which improves the utilization of pre-trained features. To evaluate the performance of the model, we conducted extensive experiments on benchmark datasets, which demonstrated that MCRN significantly improves the APE task.
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References
Bao, J., Liang, B., Sun, J., Zhang, Y., Yang, M., Xu, R.: Argument pair extraction with mutual guidance and inter-sentence relation graph. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 3923–3934 (2021)
Chalaguine, L.A., Hunter, A., Potts, H., Hamilton, F.: Impact of argument type and concerns in argumentation with a chatbot. In: 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), pp. 1557–1562 (2019). https://doi.org/10.1109/ICTAI.2019.00224
Chen, S., Liu, J., Wang, Y., Zhang, W., Chi, Z.: Synchronous double-channel recurrent network for aspect-opinion pair extraction. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 6515–6524. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.582. http://www.aclanthology.org/2020.acl-main.582
Cheng, L., Bing, L., He, R., Yu, Q., Zhang, Y., Si, L.: IAM: a comprehensive and large-scale dataset for integrated argument mining tasks. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2277–2287 (2022)
Cheng, L., Bing, L., Yu, Q., Lu, W., Si, L.: APE: argument pair extraction from peer review and rebuttal via multi-task learning. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 7000–7011 (2020)
Cheng, L., Wu, T., Bing, L., Si, L.: Argument pair extraction via attention-guided multi-layer multi-cross encoding. In: 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), pp. 6341–6353 (2021)
Dumani, L., Neumann, P.J., Schenkel, R.: A framework for argument retrieval. In: Jose, J.M., et al. (eds.) ECIR 2020. LNCS, vol. 12035, pp. 431–445. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-45439-5_29
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)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256. JMLR Workshop and Conference Proceedings (2010)
Ji, L., Wei, Z., Li, J., Zhang, Q., Huang, X.J.: Discrete argument representation learning for interactive argument pair identification. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 5467–5478 (2021)
Lafferty, J., McCallum, A., Pereira, F.C.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of 18th International Conference on Machine Learning, pp. 282–289 (2001)
Lawrence, J., Reed, C.: Argument mining: a survey. Comput. Linguist. 45(4), 765–818 (2020)
Levy, R., Bilu, Y., Hershcovich, D., Aharoni, E., Slonim, N.: Context dependent claim detection. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pp. 1489–1500 (2014)
Levy, R., Bilu, Y., Hershcovich, D., Aharoni, E., Slonim, N.: Context dependent claim detection. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, Dublin, Ireland, pp. 1489–1500. Dublin City University and Association for Computational Linguistics (2014). http://www.aclanthology.org/C14-1141
Miwa, M., Sasaki, Y.: Modeling joint entity and relation extraction with table representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1858–1869 (2014)
Persing, I., Ng, V.: Why can’t you convince me? Modeling weaknesses in unpersuasive arguments. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, pp. 4082–4088 (2017). https://doi.org/10.24963/ijcai.2017/570
P Petasis, G., Karkaletsis, V.: Identifying argument components through TextRank. In: Proceedings of the Third Workshop on Argument Mining (ArgMining2016), Berlin, Germany, pp. 94–102. Association for Computational Linguistics (2016). https://doi.org/10.18653/v1/W16-2811. http://www.aclanthology.org/W16-2811
Poudyal, P.: A machine learning approach to argument mining in legal documents. In: Pagallo, U., Palmirani, M., Casanovas, P., Sartor, G., Villata, S. (eds.) AICOL 2015-2017. LNCS (LNAI), vol. 10791, pp. 443–450. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00178-0_30
Poudyal, P., Šavelka, J., Ieven, A., Moens, M.F., Goncalves, T., Quaresma, P.: ECHR: legal corpus for argument mining. In: Proceedings of the 7th Workshop on Argument Mining, pp. 67–75 (2020)
Song, W., Song, Z., Fu, R., Liu, L., Cheng, M., Liu, T.: Discourse self-attention for discourse element identification in argumentative student essays. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 2820–2830 (2020)
Stab, C., Gurevych, I.: Identifying argumentative discourse structures in persuasive essays. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 46–56 (2014)
Tan, C., Niculae, V., Danescu-Niculescu-Mizil, C., Lee, L.: Winning arguments: interaction dynamics and persuasion strategies in good-faith online discussions. In: Proceedings of the 25th International Conference on World Wide Web, pp. 613–624 (2016)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Wachsmuth, H., et al.: Computational argumentation quality assessment in natural language. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, Valencia, Spain, pp. 176–187. Association for Computational Linguistics (2017). http://www.aclanthology.org/E17-1017
Wang, J., Lu, W.: Two are better than one: joint entity and relation extraction with table-sequence encoders. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1706–1721 (2020)
Wei, Z., et al.: A preliminary study of disputation behavior in online debating forum. In: Proceedings of the Third Workshop on Argument Mining (ArgMining2016), Berlin, Germany, pp. 166–171. Association for Computational Linguistics (2016). https://doi.org/10.18653/v1/W16-2820. http://www.aclanthology.org/W16-2820
Acknowledgement
The work reported in this paper was partially supported by a National Natural Science Foundation of China project 61963004.
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He, N., Chen, Q., Yu, Q., Han, Z. (2023). Multi-fusion Recurrent Network for Argument Pair Extraction. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14259. Springer, Cham. https://doi.org/10.1007/978-3-031-44223-0_9
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