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        <title>Deep contextualized word representations for detecting sarcasm and irony</title>
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    <name type="personal">
        <namePart type="given">Suzana</namePart>
        <namePart type="family">Ilić</namePart>
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            <title>Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis</title>
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            <namePart type="given">Alexandra</namePart>
            <namePart type="family">Balahur</namePart>
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            <namePart type="given">Saif</namePart>
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    <abstract>Predicting context-dependent and non-literal utterances like sarcastic and ironic expressions still remains a challenging task in NLP, as it goes beyond linguistic patterns, encompassing common sense and shared knowledge as crucial components. To capture complex morpho-syntactic features that can usually serve as indicators for irony or sarcasm across dynamic contexts, we propose a model that uses character-level vector representations of words, based on ELMo. We test our model on 7 different datasets derived from 3 different data sources, providing state-of-the-art performance in 6 of them, and otherwise offering competitive results.</abstract>
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    <identifier type="doi">10.18653/v1/W18-6202</identifier>
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%0 Conference Proceedings
%T Deep contextualized word representations for detecting sarcasm and irony
%A Ilić, Suzana
%A Marrese-Taylor, Edison
%A Balazs, Jorge
%A Matsuo, Yutaka
%Y Balahur, Alexandra
%Y Mohammad, Saif M.
%Y Hoste, Veronique
%Y Klinger, Roman
%S Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F ilic-etal-2018-deep
%X Predicting context-dependent and non-literal utterances like sarcastic and ironic expressions still remains a challenging task in NLP, as it goes beyond linguistic patterns, encompassing common sense and shared knowledge as crucial components. To capture complex morpho-syntactic features that can usually serve as indicators for irony or sarcasm across dynamic contexts, we propose a model that uses character-level vector representations of words, based on ELMo. We test our model on 7 different datasets derived from 3 different data sources, providing state-of-the-art performance in 6 of them, and otherwise offering competitive results.
%R 10.18653/v1/W18-6202
%U https://aclanthology.org/W18-6202/
%U https://doi.org/10.18653/v1/W18-6202
%P 2-7

Markdown (Informal)

[Deep contextualized word representations for detecting sarcasm and irony](https://aclanthology.org/W18-6202/) (Ilić et al., WASSA 2018)

• Deep contextualized word representations for detecting sarcasm and irony (Ilić et al., WASSA 2018)

ACL

• Suzana Ilić, Edison Marrese-Taylor, Jorge Balazs, and Yutaka Matsuo. 2018. Deep contextualized word representations for detecting sarcasm and irony. In Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 2–7, Brussels, Belgium. Association for Computational Linguistics.