Habernal, Ivan ; Gurevych, Iryna (2016)
Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM.
Berlin, Germany
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
We propose a new task in the field of computational argumentation in which we investigate qualitative properties of Web arguments, namely their convincingness. We cast the problem as relation classification, where a pair of arguments having the same stance to the same prompt is judged. We annotate a large datasets of 16k pairs of arguments over 32 topics and investigate whether the relation "A is more convincing than B" exhibits properties of total ordering; these findings are used as global constraints for cleaning the crowdsourced data. We propose two tasks: (1) predicting which argument from an argument pair is more convincing and (2) ranking all arguments to the topic based on their convincingness. We experiment with feature-rich SVM and bidirectional LSTM and obtain 0.76-0.78 accuracy and 0.35-0.40 Spearman's correlation in a cross-topic evaluation. We release the newly created corpus UKPConvArg1 and the experimental software under open licenses.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2016 |
| Autor(en): | Habernal, Ivan ; Gurevych, Iryna |
| Art des Eintrags: | Bibliographie |
| Titel: | Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM |
| Sprache: | Englisch |
| Publikationsjahr: | August 2016 |
| Verlag: | Association for Computational Linguistics |
| Buchtitel: | Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) |
| Veranstaltungsort: | Berlin, Germany |
| URL / URN: | http://www.aclweb.org/anthology/P16-1150 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | We propose a new task in the field of computational argumentation in which we investigate qualitative properties of Web arguments, namely their convincingness. We cast the problem as relation classification, where a pair of arguments having the same stance to the same prompt is judged. We annotate a large datasets of 16k pairs of arguments over 32 topics and investigate whether the relation "A is more convincing than B" exhibits properties of total ordering; these findings are used as global constraints for cleaning the crowdsourced data. We propose two tasks: (1) predicting which argument from an argument pair is more convincing and (2) ranking all arguments to the topic based on their convincingness. We experiment with feature-rich SVM and bidirectional LSTM and obtain 0.76-0.78 accuracy and 0.35-0.40 Spearman's correlation in a cross-topic evaluation. We release the newly created corpus UKPConvArg1 and the experimental software under open licenses. |
| Freie Schlagworte: | UKP_reviewed;UKP_a_ArMin |
| ID-Nummer: | TUD-CS-2016-0104 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung DFG-Graduiertenkollegs DFG-Graduiertenkollegs > Graduiertenkolleg 1994 Adaptive Informationsaufbereitung aus heterogenen Quellen |
| Hinterlegungsdatum: | 31 Dez 2016 14:29 |
| Letzte Änderung: | 24 Jan 2020 12:03 |
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