Abstract
We propose a novel factor graph model for argument mining, designed for settings in which the argumentative relations in a document do not necessarily form a tree structure. (This is the case in over 20% of the web comments dataset we release.) Our model jointly learns elementary unit type classification and argumentative relation prediction. Moreover, our model supports SVM and RNN parametrizations, can enforce structure constraints (e.g., transitivity), and can express dependencies between adjacent relations and propositions. Our approaches outperform unstructured baselines in both web comments and argumentative essay datasets.- Anthology ID:
- P17-1091
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 985–995
- Language:
- URL:
- https://aclanthology.org/P17-1091
- DOI:
- 10.18653/v1/P17-1091
- Cite (ACL):
- Vlad Niculae, Joonsuk Park, and Claire Cardie. 2017. Argument Mining with Structured SVMs and RNNs. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 985–995, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Argument Mining with Structured SVMs and RNNs (Niculae et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/P17-1091.pdf
- Code
- vene/marseille