Neural End-to-End Learning for Computational Argumentation Mining

Steffen Eger, Johannes Daxenberger, Iryna Gurevych


Abstract
We investigate neural techniques for end-to-end computational argumentation mining (AM). We frame AM both as a token-based dependency parsing and as a token-based sequence tagging problem, including a multi-task learning setup. Contrary to models that operate on the argument component level, we find that framing AM as dependency parsing leads to subpar performance results. In contrast, less complex (local) tagging models based on BiLSTMs perform robustly across classification scenarios, being able to catch long-range dependencies inherent to the AM problem. Moreover, we find that jointly learning ‘natural’ subtasks, in a multi-task learning setup, improves performance.
Anthology ID:
P17-1002
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
11–22
Language:
URL:
https://aclanthology.org/P17-1002
DOI:
10.18653/v1/P17-1002
Bibkey:
Cite (ACL):
Steffen Eger, Johannes Daxenberger, and Iryna Gurevych. 2017. Neural End-to-End Learning for Computational Argumentation Mining. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11–22, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Neural End-to-End Learning for Computational Argumentation Mining (Eger et al., ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-1002.pdf
Note:
 P17-1002.Notes.pdf
Video:
 https://vimeo.com/234953034
Code
 UKPLab/acl2017-neural_end2end_AM +  additional community code