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        <title>Importance of Self-Attention for Sentiment Analysis</title>
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        <namePart type="given">Frédérik</namePart>
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    <name type="personal">
        <namePart type="given">Philippe</namePart>
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    <name type="personal">
        <namePart type="given">François</namePart>
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            <namePart type="given">Tal</namePart>
            <namePart type="family">Linzen</namePart>
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            <namePart type="given">Grzegorz</namePart>
            <namePart type="family">Chrupała</namePart>
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            <namePart type="given">Afra</namePart>
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    <abstract>Despite their superior performance, deep learning models often lack interpretability. In this paper, we explore the modeling of insightful relations between words, in order to understand and enhance predictions. To this effect, we propose the Self-Attention Network (SANet), a flexible and interpretable architecture for text classification. Experiments indicate that gains obtained by self-attention is task-dependent. For instance, experiments on sentiment analysis tasks showed an improvement of around 2% when using self-attention compared to a baseline without attention, while topic classification showed no gain. Interpretability brought forward by our architecture highlighted the importance of neighboring word interactions to extract sentiment.</abstract>
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%0 Conference Proceedings
%T Importance of Self-Attention for Sentiment Analysis
%A Letarte, Gaël
%A Paradis, Frédérik
%A Giguère, Philippe
%A Laviolette, François
%Y Linzen, Tal
%Y Chrupała, Grzegorz
%Y Alishahi, Afra
%S Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F letarte-etal-2018-importance
%X Despite their superior performance, deep learning models often lack interpretability. In this paper, we explore the modeling of insightful relations between words, in order to understand and enhance predictions. To this effect, we propose the Self-Attention Network (SANet), a flexible and interpretable architecture for text classification. Experiments indicate that gains obtained by self-attention is task-dependent. For instance, experiments on sentiment analysis tasks showed an improvement of around 2% when using self-attention compared to a baseline without attention, while topic classification showed no gain. Interpretability brought forward by our architecture highlighted the importance of neighboring word interactions to extract sentiment.
%R 10.18653/v1/W18-5429
%U https://aclanthology.org/W18-5429
%U https://doi.org/10.18653/v1/W18-5429
%P 267-275

Markdown (Informal)

[Importance of Self-Attention for Sentiment Analysis](https://aclanthology.org/W18-5429) (Letarte et al., EMNLP 2018)

• Importance of Self-Attention for Sentiment Analysis (Letarte et al., EMNLP 2018)

ACL

• Gaël Letarte, Frédérik Paradis, Philippe Giguère, and François Laviolette. 2018. Importance of Self-Attention for Sentiment Analysis. In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pages 267–275, Brussels, Belgium. Association for Computational Linguistics.