@inproceedings{wang-etal-2019-aspect,
title = "Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network",
author = "Wang, Jingjing and
Sun, Changlong and
Li, Shoushan and
Liu, Xiaozhong and
Si, Luo and
Zhang, Min and
Zhou, Guodong",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1345",
doi = "10.18653/v1/P19-1345",
pages = "3548--3557",
abstract = "In the literature, existing studies on aspect sentiment classification (ASC) focus on individual non-interactive reviews. This paper extends the research to interactive reviews and proposes a new research task, namely Aspect Sentiment Classification towards Question-Answering (ASC-QA), for real-world applications. This new task aims to predict sentiment polarities for specific aspects from interactive QA style reviews. In particular, a high-quality annotated corpus is constructed for ASC-QA to facilitate corresponding research. On this basis, a Reinforced Bidirectional Attention Network (RBAN) approach is proposed to address two inherent challenges in ASC-QA, i.e., semantic matching between question and answer, and data noise. Experimental results demonstrate the great advantage of the proposed approach to ASC-QA against several state-of-the-art baselines.",
}
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<abstract>In the literature, existing studies on aspect sentiment classification (ASC) focus on individual non-interactive reviews. This paper extends the research to interactive reviews and proposes a new research task, namely Aspect Sentiment Classification towards Question-Answering (ASC-QA), for real-world applications. This new task aims to predict sentiment polarities for specific aspects from interactive QA style reviews. In particular, a high-quality annotated corpus is constructed for ASC-QA to facilitate corresponding research. On this basis, a Reinforced Bidirectional Attention Network (RBAN) approach is proposed to address two inherent challenges in ASC-QA, i.e., semantic matching between question and answer, and data noise. Experimental results demonstrate the great advantage of the proposed approach to ASC-QA against several state-of-the-art baselines.</abstract>
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%0 Conference Proceedings
%T Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network
%A Wang, Jingjing
%A Sun, Changlong
%A Li, Shoushan
%A Liu, Xiaozhong
%A Si, Luo
%A Zhang, Min
%A Zhou, Guodong
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F wang-etal-2019-aspect
%X In the literature, existing studies on aspect sentiment classification (ASC) focus on individual non-interactive reviews. This paper extends the research to interactive reviews and proposes a new research task, namely Aspect Sentiment Classification towards Question-Answering (ASC-QA), for real-world applications. This new task aims to predict sentiment polarities for specific aspects from interactive QA style reviews. In particular, a high-quality annotated corpus is constructed for ASC-QA to facilitate corresponding research. On this basis, a Reinforced Bidirectional Attention Network (RBAN) approach is proposed to address two inherent challenges in ASC-QA, i.e., semantic matching between question and answer, and data noise. Experimental results demonstrate the great advantage of the proposed approach to ASC-QA against several state-of-the-art baselines.
%R 10.18653/v1/P19-1345
%U https://aclanthology.org/P19-1345
%U https://doi.org/10.18653/v1/P19-1345
%P 3548-3557
Markdown (Informal)
[Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network](https://aclanthology.org/P19-1345) (Wang et al., ACL 2019)
ACL