Argument Mining with Structured SVMs and RNNs

Vlad Niculae, Joonsuk Park, Claire Cardie


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/P17-1091.pdf
Video:
 https://aclanthology.org/P17-1091.mp4
Code
 vene/marseille