@inproceedings{bao-etal-2021-neural,
title = "A Neural Transition-based Model for Argumentation Mining",
author = "Bao, Jianzhu and
Fan, Chuang and
Wu, Jipeng and
Dang, Yixue and
Du, Jiachen and
Xu, Ruifeng",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "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)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.497",
doi = "10.18653/v1/2021.acl-long.497",
pages = "6354--6364",
abstract = "The goal of argumentation mining is to automatically extract argumentation structures from argumentative texts. Most existing methods determine argumentative relations by exhaustively enumerating all possible pairs of argument components, which suffer from low efficiency and class imbalance. Moreover, due to the complex nature of argumentation, there is, so far, no universal method that can address both tree and non-tree structured argumentation. Towards these issues, we propose a neural transition-based model for argumentation mining, which incrementally builds an argumentation graph by generating a sequence of actions, avoiding inefficient enumeration operations. Furthermore, our model can handle both tree and non-tree structured argumentation without introducing any structural constraints. Experimental results show that our model achieves the best performance on two public datasets of different structures.",
}
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<abstract>The goal of argumentation mining is to automatically extract argumentation structures from argumentative texts. Most existing methods determine argumentative relations by exhaustively enumerating all possible pairs of argument components, which suffer from low efficiency and class imbalance. Moreover, due to the complex nature of argumentation, there is, so far, no universal method that can address both tree and non-tree structured argumentation. Towards these issues, we propose a neural transition-based model for argumentation mining, which incrementally builds an argumentation graph by generating a sequence of actions, avoiding inefficient enumeration operations. Furthermore, our model can handle both tree and non-tree structured argumentation without introducing any structural constraints. Experimental results show that our model achieves the best performance on two public datasets of different structures.</abstract>
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%0 Conference Proceedings
%T A Neural Transition-based Model for Argumentation Mining
%A Bao, Jianzhu
%A Fan, Chuang
%A Wu, Jipeng
%A Dang, Yixue
%A Du, Jiachen
%A Xu, Ruifeng
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S 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)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F bao-etal-2021-neural
%X The goal of argumentation mining is to automatically extract argumentation structures from argumentative texts. Most existing methods determine argumentative relations by exhaustively enumerating all possible pairs of argument components, which suffer from low efficiency and class imbalance. Moreover, due to the complex nature of argumentation, there is, so far, no universal method that can address both tree and non-tree structured argumentation. Towards these issues, we propose a neural transition-based model for argumentation mining, which incrementally builds an argumentation graph by generating a sequence of actions, avoiding inefficient enumeration operations. Furthermore, our model can handle both tree and non-tree structured argumentation without introducing any structural constraints. Experimental results show that our model achieves the best performance on two public datasets of different structures.
%R 10.18653/v1/2021.acl-long.497
%U https://aclanthology.org/2021.acl-long.497
%U https://doi.org/10.18653/v1/2021.acl-long.497
%P 6354-6364
Markdown (Informal)
[A Neural Transition-based Model for Argumentation Mining](https://aclanthology.org/2021.acl-long.497) (Bao et al., ACL-IJCNLP 2021)
ACL
- Jianzhu Bao, Chuang Fan, Jipeng Wu, Yixue Dang, Jiachen Du, and Ruifeng Xu. 2021. A Neural Transition-based Model for Argumentation Mining. 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), pages 6354–6364, Online. Association for Computational Linguistics.