%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