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        <title>Learning Sentiment Memories for Sentiment Modification without Parallel Data</title>
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        <namePart type="given">Yi</namePart>
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        <namePart type="given">Jingjing</namePart>
        <namePart type="family">Xu</namePart>
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    <name type="personal">
        <namePart type="given">Pengcheng</namePart>
        <namePart type="family">Yang</namePart>
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    <name type="personal">
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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    <abstract>The task of sentiment modification requires reversing the sentiment of the input and preserving the sentiment-independent content. However, aligned sentences with the same content but different sentiments are usually unavailable. Due to the lack of such parallel data, it is hard to extract sentiment independent content and reverse the sentiment in an unsupervised way. Previous work usually can not reconcile sentiment transformation and content preservation. In this paper, motivated by the fact the non-emotional context (e.g., “staff”) provides strong cues for the occurrence of emotional words (e.g., “friendly”), we propose a novel method that automatically extracts appropriate sentiment information from learned sentiment memories according to the specific context. Experiments show that our method substantially improves the content preservation degree and achieves the state-of-the-art performance.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1138</identifier>
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        <date>2018-oct-nov</date>
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            <start>1103</start>
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%0 Conference Proceedings
%T Learning Sentiment Memories for Sentiment Modification without Parallel Data
%A Zhang, Yi
%A Xu, Jingjing
%A Yang, Pengcheng
%A Sun, Xu
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F zhang-etal-2018-learning
%X The task of sentiment modification requires reversing the sentiment of the input and preserving the sentiment-independent content. However, aligned sentences with the same content but different sentiments are usually unavailable. Due to the lack of such parallel data, it is hard to extract sentiment independent content and reverse the sentiment in an unsupervised way. Previous work usually can not reconcile sentiment transformation and content preservation. In this paper, motivated by the fact the non-emotional context (e.g., “staff”) provides strong cues for the occurrence of emotional words (e.g., “friendly”), we propose a novel method that automatically extracts appropriate sentiment information from learned sentiment memories according to the specific context. Experiments show that our method substantially improves the content preservation degree and achieves the state-of-the-art performance.
%R 10.18653/v1/D18-1138
%U https://aclanthology.org/D18-1138
%U https://doi.org/10.18653/v1/D18-1138
%P 1103-1108

Markdown (Informal)

[Learning Sentiment Memories for Sentiment Modification without Parallel Data](https://aclanthology.org/D18-1138) (Zhang et al., EMNLP 2018)

• Learning Sentiment Memories for Sentiment Modification without Parallel Data (Zhang et al., EMNLP 2018)

ACL

• Yi Zhang, Jingjing Xu, Pengcheng Yang, and Xu Sun. 2018. Learning Sentiment Memories for Sentiment Modification without Parallel Data. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1103–1108, Brussels, Belgium. Association for Computational Linguistics.