TU Darmstadt / ULB / TUbiblio

Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM

Habernal, Ivan and Gurevych, Iryna (2016):
Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM.
In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Berlin, Germany, [Online-Edition: http://www.aclweb.org/anthology/P16-1150],
[Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2016
Creators: Habernal, Ivan and Gurevych, Iryna
Title: Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM
Language: English
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.

Title of Book: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Publisher: Association for Computational Linguistics
Uncontrolled Keywords: UKP_reviewed;UKP_a_ArMin
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Ubiquitous Knowledge Processing
DFG-Graduiertenkollegs
DFG-Graduiertenkollegs > Research Training Group 1994 Adaptive Preparation of Information from Heterogeneous Sources
Event Location: Berlin, Germany
Date Deposited: 31 Dec 2016 14:29
Official URL: http://www.aclweb.org/anthology/P16-1150
Identification Number: TUD-CS-2016-0104
Related URLs:
Export:
Suche nach Titel in: TUfind oder in Google

Optionen (nur für Redakteure)

View Item View Item