End-to-End Argument Mining for Discussion Threads Based on Parallel Constrained Pointer Architecture
書誌事項
- 公開日
- 2018-01-01
- DOI
-
- 10.18653/v1/w18-5202
- 10.48550/arxiv.1809.00563
- 公開者
- Association for Computational Linguistics (ACL)
説明
Argument Mining (AM) is a relatively recent discipline, which concentrates on extracting claims or premises from discourses, and inferring their structures. However, many existing works do not consider micro-level AM studies on discussion threads sufficiently. In this paper, we tackle AM for discussion threads. Our main contributions are follows: (1) A novel combination scheme focusing on micro-level inner- and inter- post schemes for a discussion thread. (2) Annotation of large-scale civic discussion threads with the scheme. (3) Parallel constrained pointer architecture (PCPA), a novel end-to-end technique to discriminate sentence types, inner-post relations, and inter-post interactions simultaneously. The experimental results demonstrate that our proposed model shows better accuracy in terms of relations extraction, in comparison to existing state-of-the-art models.
accepted at the 5th Workshop on Argument Mining at EMNLP 2018
収録刊行物
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- Proceedings of the 5th Workshop on Argument Mining
-
Proceedings of the 5th Workshop on Argument Mining 11-21, 2018-01-01
Association for Computational Linguistics (ACL)
