@inproceedings{siddiqua-etal-2019-tweet,
title = "{T}weet Stance Detection Using an Attention based Neural Ensemble Model",
author = "Siddiqua, Umme Aymun and
Chy, Abu Nowshed and
Aono, Masaki",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1185",
doi = "10.18653/v1/N19-1185",
pages = "1868--1873",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Tweet Stance Detection Using an Attention based Neural Ensemble Model
%A Siddiqua, Umme Aymun
%A Chy, Abu Nowshed
%A Aono, Masaki
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S 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)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F siddiqua-etal-2019-tweet
%X 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.
%R 10.18653/v1/N19-1185
%U https://aclanthology.org/N19-1185
%U https://doi.org/10.18653/v1/N19-1185
%P 1868-1873
Markdown (Informal)
[Tweet Stance Detection Using an Attention based Neural Ensemble Model](https://aclanthology.org/N19-1185) (Siddiqua et al., NAACL 2019)
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.