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Habernal, Ivan; Gurevych, Iryna:

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) Stroudsburg, PA : Association for Computational Linguistics (2016) , 1589-1599

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4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings

Algorithmus, Argumentation, Automatisierung, Computerlinguistik, Kommunikation, Online, Prognose, Qualität, Rhetorik, Soziale Software, Textanalyse, Überzeugung, World wide web 2.0

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.)

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