Abstract
In order to determine argument structure in text, one must understand how individual components of the overall argument are linked. This work presents the first neural network-based approach to link extraction in argument mining. Specifically, we propose a novel architecture that applies Pointer Network sequence-to-sequence attention modeling to structural prediction in discourse parsing tasks. We then develop a joint model that extends this architecture to simultaneously address the link extraction task and the classification of argument components. The proposed joint model achieves state-of-the-art results on two separate evaluation corpora, showing far superior performance than the previously proposed corpus-specific and heavily feature-engineered models. Furthermore, our results demonstrate that jointly optimizing for both tasks is crucial for high performance.- Anthology ID:
- D17-1143
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1364–1373
- Language:
- URL:
- https://aclanthology.org/D17-1143
- DOI:
- 10.18653/v1/D17-1143
- Cite (ACL):
- Peter Potash, Alexey Romanov, and Anna Rumshisky. 2017. Here’s My Point: Joint Pointer Architecture for Argument Mining. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1364–1373, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Here’s My Point: Joint Pointer Architecture for Argument Mining (Potash et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/D17-1143.pdf