{"type":"ScholarlyArticle","title":"End-to-End Adversarial Memory Network for Cross-domain Sentiment Classification","authors":[{"name":"Zheng Li","slug":"zheng-li","given":"Zheng","family":"Li","affiliation":"Department of Computer Science and Engineering , Hong Kong University of Science and Technology"},{"name":"Yu Zhang","slug":"yu-zhang","given":"Yu","family":"Zhang","affiliation":"Department of Computer Science and Engineering , Hong Kong University of Science and Technology"},{"name":"Ying Wei","slug":"ying-wei","given":"Ying","family":"Wei","affiliation":"Department of Computer Science and Engineering , Hong Kong University of Science and Technology"},{"name":"Yuxiang Wu","slug":"yuxiang-wu","given":"Yuxiang","family":"Wu","affiliation":"Department of Computer Science and Engineering , Hong Kong University of Science and Technology"},{"name":"Qiang Yang","slug":"qiang-yang","given":"Qiang","family":"Yang","affiliation":"Department of Computer Science and Engineering , Hong Kong University of Science and Technology"}],"abstract":"Domain adaptation tasks such as cross-domain sentiment classification have raised much attention in recent years. Due to the domain discrepancy, a sentiment classifier trained in a source domain may not work well when directly applied to a target domain. Traditional methods need to manually select pivots, which behave in the same way for discriminative learning in both domains. Recently, deep learning methods have been proposed to learn a representation shared by domains. However, they lack the interpretability to directly identify the pivots. To address the problem, we introduce an end-to-end Adversarial Memory Network (AMN) for cross-domain sentiment classification. Unlike existing methods, our approach can automatically capture the pivots using an attention mechanism. Our framework consists of two parameter-shared memory networks: one is for sentiment classification and the other is for domain classification. The two networks are jointly trained so that the selected features minimize the sentiment classification error and at the same time make the domain classifier indiscriminative between the representations from the source or target domains. Moreover, unlike deep learning methods that cannot tell us which words are the pivots, our approach can offer a direct visualization of them. Experiments on the Amazon review dataset demonstrate that our approach can significantly outperform state-of-the-art methods.","doi":"10.24963/ijcai.2017/311","url":"https://ijcai.org/proceedings/2017/311","pdf":"https://ijcai.org/proceedings/2017/0311.pdf","markdown":"https://ijcai.org/proceedings/2017/311.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/311","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":311,"pages":{"first":2237,"last":2243},"keywords":["Natural Language Processing: Sentiment Analysis and Text Mining","Machine Learning: Deep Learning","Machine Learning: Transfer, Adaptation, Multi-task Learning"],"videos":[],"references":[{"position":1,"raw":"[Blitzer et al., 2006] John Blitzer, Ryan McDonald, and Fer- nando Pereira. 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