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Autor:
Eger, Steffen; Daxenberger, Johannes; Gurevych, Iryna:

Titel:
Neural end-to-end learning for computational argumentation mining

Quelle:
In: Association for Computational Linguistics (Hrsg.): The 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017) Stroudsburg, PA : Association for Computational Linguistics (2017) , 11-22

URL des Volltextes:
https://aclanthology.info/pdf/P/P17/P17-1002.pdf

Sprache:
Englisch

Dokumenttyp:
4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings

Schlagwörter:
Argumentation, Automatisierung, Computerlinguistik, Data Mining, Klassifikation, Rhetorik, Semantik, Textanalyse


Abstract(original):
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 BiL-STMs 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. (DIPF/Orig.)


DIPF-Abteilung:
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last modified Nov 11, 2016