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Stance Detection with Bidirectional Conditional Encoding

Augenstein, I; Rocktäschel, T; Vlachos, A; Bontcheva, K; (2016) Stance Detection with Bidirectional Conditional Encoding. In: Su, Jian and Duh, Kevin and Carreras, Xavier, (eds.) Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. (pp. pp. 876-885). The Association for Computational Linguistics: Texas, USA. Green open access

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Abstract

Stance detection is the task of classifying the attitude expressed in a text towards a target such as Hillary Clinton to be "positive", negative" or "neutral". Previous work has assumed that either the target is mentioned in the text or that training data for every target is given. This paper considers the more challenging version of this task, where targets are not always mentioned and no training data is available for the test targets. We experiment with conditional LSTM encoding, which builds a representation of the tweet that is dependent on the target, and demonstrate that it outperforms encoding the tweet and the target independently. Performance is improved further when the conditional model is augmented with bidirectional encoding. We evaluate our approach on the SemEval 2016 Task 6 Twitter Stance Detection corpus achieving performance second best only to a system trained on semi-automatically labelled tweets for the test target. When such weak supervision is added, our approach achieves state-of-the-art results.

Type: Proceedings paper
Title: Stance Detection with Bidirectional Conditional Encoding
Event: 2016 Conference on Empirical Methods in Natural Language Processing
Open access status: An open access version is available from UCL Discovery
DOI: 10.18653/v1/D16-1084
Publisher version: https://www.aclweb.org/anthology/D16-1084
Language: English
Additional information: Copyright © The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed.
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/10058846
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