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Author(s): Habernal, Ivan; Gurevych, Iryna
Title: Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM
In: Association for Computational Linguistics (Hrsg.): Proceedings of the 54th annual meeting of the Association for Computational Linguistics (ACL 2016): Long papers, Stroudsburg; PA: Association for Computational Linguistics, 2016 , S. 1589-1599
URL: http://www.aclweb.org/anthology/P16-1150
Publication Type: 4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Language: Englisch
Keywords: Algorithmus; Argumentation; Automatisierung; Computerlinguistik; Kommunikation; Online; Prognose; Qualität; Rhetorik; Soziale Software; Textanalyse; Überzeugung; World wide web 2.0
Abstract (english): 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. (DIPF/Orig.)
DIPF-Departments: Informationszentrum Bildung