Eger, Steffen and Daxenberger, Johannes and Gurevych, Iryna (2017):
Neural End-to-End Learning for Computational Argumentation Mining.
Volume 1: Long PapersIn: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), Association for Computational Linguistics, Vancouver, Canada, pp. 11-22, [Online-Edition: http://aclweb.org/anthology/P17-1002],
[Conference or Workshop Item]
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.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2017 |
| Creators: | Eger, Steffen and Daxenberger, Johannes and Gurevych, Iryna |
| Title: | Neural End-to-End Learning for Computational Argumentation Mining |
| Language: | English |
| 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. |
| Title of Book: | Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017) |
| Volume: | Volume 1: Long Papers |
| Publisher: | Association for Computational Linguistics |
| Uncontrolled Keywords: | UKP_a_DLinNLP, UKP_a_ArMin, reviewed, UKP_p_ArgumenText |
| Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Ubiquitous Knowledge Processing |
| Event Location: | Vancouver, Canada |
| Date Deposited: | 31 Mar 2017 14:02 |
| Official URL: | http://aclweb.org/anthology/P17-1002 |
| Identification Number: | TUD-CS-2017-0070 |
| Related URLs: | |
| Projects: | ArgumenText |
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