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        <title>Leveraging Auxiliary Tasks for Document-Level Cross-Domain Sentiment Classification</title>
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    <name type="personal">
        <namePart type="given">Jianfei</namePart>
        <namePart type="family">Yu</namePart>
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        <namePart type="given">Jing</namePart>
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        <dateIssued>2017-11</dateIssued>
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            <title>Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)</title>
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        <name type="personal">
            <namePart type="given">Greg</namePart>
            <namePart type="family">Kondrak</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Taro</namePart>
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            <publisher>Asian Federation of Natural Language Processing</publisher>
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    <abstract>In this paper, we study domain adaptation with a state-of-the-art hierarchical neural network for document-level sentiment classification. We first design a new auxiliary task based on sentiment scores of domain-independent words. We then propose two neural network architectures to respectively induce document embeddings and sentence embeddings that work well for different domains. When these document and sentence embeddings are used for sentiment classification, we find that with both pseudo and external sentiment lexicons, our proposed methods can perform similarly to or better than several highly competitive domain adaptation methods on a benchmark dataset of product reviews.</abstract>
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        <date>2017-11</date>
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%0 Conference Proceedings
%T Leveraging Auxiliary Tasks for Document-Level Cross-Domain Sentiment Classification
%A Yu, Jianfei
%A Jiang, Jing
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F yu-jiang-2017-leveraging
%X In this paper, we study domain adaptation with a state-of-the-art hierarchical neural network for document-level sentiment classification. We first design a new auxiliary task based on sentiment scores of domain-independent words. We then propose two neural network architectures to respectively induce document embeddings and sentence embeddings that work well for different domains. When these document and sentence embeddings are used for sentiment classification, we find that with both pseudo and external sentiment lexicons, our proposed methods can perform similarly to or better than several highly competitive domain adaptation methods on a benchmark dataset of product reviews.
%U https://aclanthology.org/I17-1066
%P 654-663

Markdown (Informal)

[Leveraging Auxiliary Tasks for Document-Level Cross-Domain Sentiment Classification](https://aclanthology.org/I17-1066) (Yu & Jiang, IJCNLP 2017)

• Leveraging Auxiliary Tasks for Document-Level Cross-Domain Sentiment Classification (Yu & Jiang, IJCNLP 2017)

ACL

• Jianfei Yu and Jing Jiang. 2017. Leveraging Auxiliary Tasks for Document-Level Cross-Domain Sentiment Classification. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 654–663, Taipei, Taiwan. Asian Federation of Natural Language Processing.