Tweet Stance Detection Using an Attention based Neural Ensemble Model

Umme Aymun Siddiqua, Abu Nowshed Chy, Masaki Aono

Abstract
Stance detection in twitter aims at mining user stances expressed in a tweet towards a single or multiple target entities. To tackle this problem, most of the prior studies have been explored the traditional deep learning models, e.g., LSTM and GRU. However, in compared to these traditional approaches, recently proposed densely connected Bi-LSTM and nested LSTMs architectures effectively address the vanishing-gradient and overfitting problems as well as dealing with long-term dependencies. In this paper, we propose a neural ensemble model that adopts the strengths of these two LSTM variants to learn better long-term dependencies, where each module coupled with an attention mechanism that amplifies the contribution of important elements in the final representation. We also employ a multi-kernel convolution on top of them to extract the higher-level tweet representations. Results of extensive experiments on single and multi-target stance detection datasets show that our proposed method achieves substantial improvement over the current state-of-the-art deep learning based methods.
Anthology ID:
N19-1185
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Jill Burstein, Christy Doran, Thamar Solorio
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1868–1873
Language:
URL:
https://aclanthology.org/N19-1185
DOI:
10.18653/v1/N19-1185
Bibkey:
Cite (ACL):
Umme Aymun Siddiqua, Abu Nowshed Chy, and Masaki Aono. 2019. Tweet Stance Detection Using an Attention based Neural Ensemble Model. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1868–1873, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Tweet Stance Detection Using an Attention based Neural Ensemble Model (Siddiqua et al., NAACL 2019)
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https://preview.aclanthology.org/fix-dup-bibkey/N19-1185.pdf
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