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Autor*innen: Eger, Steffen; Daxenberger, Johannes; Gurevych, Iryna
Titel: Neural end-to-end learning for computational argumentation mining
Aus: Association for Computational Linguistics (Hrsg.): The 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017): Proceedings of the conference, vol. 1 (long papers), July 30 - August 4, 2017, Vancouver, Canada, Stroudsburg; PA: Association for Computational Linguistics, 2017 , S. 11-22
DOI: 10.18653/v1/P17-1002
URL: https://aclanthology.info/pdf/P/P17/P17-1002.pdf
Dokumenttyp: 4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Sprache: Englisch
Schlagwörter: Argumentation; Automatisierung; Computerlinguistik; Data Mining; Klassifikation; Rhetorik; Semantik; Textanalyse
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 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: Informationszentrum Bildung