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    <titleInfo>
        <title>Semantics-Preserved Data Augmentation for Aspect-Based Sentiment Analysis</title>
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    <name type="personal">
        <namePart type="given">Ting-Wei</namePart>
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    <name type="personal">
        <namePart type="given">Chung-Chi</namePart>
        <namePart type="family">Chen</namePart>
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    <name type="personal">
        <namePart type="given">Hen-Hsen</namePart>
        <namePart type="family">Huang</namePart>
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    <name type="personal">
        <namePart type="given">Hsin-Hsi</namePart>
        <namePart type="family">Chen</namePart>
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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    <abstract>Both the issues of data deficiencies and semantic consistency are important for data augmentation. Most of previous methods address the first issue, but ignore the second one. In the cases of aspect-based sentiment analysis, violation of the above issues may change the aspect and sentiment polarity. In this paper, we propose a semantics-preservation data augmentation approach by considering the importance of each word in a textual sequence according to the related aspects and sentiments. We then substitute the unimportant tokens with two replacement strategies without altering the aspect-level polarity. Our approach is evaluated on several publicly available sentiment analysis datasets and the real-world stock price/risk movement prediction scenarios. Experimental results show that our methodology achieves better performances in all datasets.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.362</identifier>
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        <date>2021-11</date>
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%0 Conference Proceedings
%T Semantics-Preserved Data Augmentation for Aspect-Based Sentiment Analysis
%A Hsu, Ting-Wei
%A Chen, Chung-Chi
%A Huang, Hen-Hsen
%A Chen, Hsin-Hsi
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F hsu-etal-2021-semantics
%X Both the issues of data deficiencies and semantic consistency are important for data augmentation. Most of previous methods address the first issue, but ignore the second one. In the cases of aspect-based sentiment analysis, violation of the above issues may change the aspect and sentiment polarity. In this paper, we propose a semantics-preservation data augmentation approach by considering the importance of each word in a textual sequence according to the related aspects and sentiments. We then substitute the unimportant tokens with two replacement strategies without altering the aspect-level polarity. Our approach is evaluated on several publicly available sentiment analysis datasets and the real-world stock price/risk movement prediction scenarios. Experimental results show that our methodology achieves better performances in all datasets.
%R 10.18653/v1/2021.emnlp-main.362
%U https://aclanthology.org/2021.emnlp-main.362
%U https://doi.org/10.18653/v1/2021.emnlp-main.362
%P 4417-4422

Markdown (Informal)

[Semantics-Preserved Data Augmentation for Aspect-Based Sentiment Analysis](https://aclanthology.org/2021.emnlp-main.362) (Hsu et al., EMNLP 2021)

• Semantics-Preserved Data Augmentation for Aspect-Based Sentiment Analysis (Hsu et al., EMNLP 2021)

ACL

• Ting-Wei Hsu, Chung-Chi Chen, Hen-Hsen Huang, and Hsin-Hsi Chen. 2021. Semantics-Preserved Data Augmentation for Aspect-Based Sentiment Analysis. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4417–4422, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.