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    <titleInfo>
        <title>Perceived and Intended Sarcasm Detection with Graph Attention Networks</title>
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    <name type="personal">
        <namePart type="given">Joan</namePart>
        <namePart type="family">Plepi</namePart>
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            <title>Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)</title>
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        <name type="personal">
            <namePart type="given">Wei</namePart>
            <namePart type="family">Xu</namePart>
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        <name type="personal">
            <namePart type="given">Alan</namePart>
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    <abstract>Existing sarcasm detection systems focus on exploiting linguistic markers, context, or user-level priors. However, social studies suggest that the relationship between the author and the audience can be equally relevant for the sarcasm usage and interpretation. In this work, we propose a framework jointly leveraging (1) a user context from their historical tweets together with (2) the social information from a user’s conversational neighborhood in an interaction graph, to contextualize the interpretation of the post. We use graph attention networks (GAT) over users and tweets in a conversation thread, combined with dense user history representations. Apart from achieving state-of-the-art results on the recently published dataset of 19k Twitter users with 30K labeled tweets, adding 10M unlabeled tweets as context, our results indicate that the model contributes to interpreting the sarcastic intentions of an author more than to predicting the sarcasm perception by others.</abstract>
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    <identifier type="doi">10.18653/v1/2021.wnut-1.12</identifier>
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        <date>2021-11</date>
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            <start>97</start>
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%0 Conference Proceedings
%T Perceived and Intended Sarcasm Detection with Graph Attention Networks
%A Plepi, Joan
%A Flek, Lucie
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online
%F plepi-flek-2021-perceived
%X Existing sarcasm detection systems focus on exploiting linguistic markers, context, or user-level priors. However, social studies suggest that the relationship between the author and the audience can be equally relevant for the sarcasm usage and interpretation. In this work, we propose a framework jointly leveraging (1) a user context from their historical tweets together with (2) the social information from a user’s conversational neighborhood in an interaction graph, to contextualize the interpretation of the post. We use graph attention networks (GAT) over users and tweets in a conversation thread, combined with dense user history representations. Apart from achieving state-of-the-art results on the recently published dataset of 19k Twitter users with 30K labeled tweets, adding 10M unlabeled tweets as context, our results indicate that the model contributes to interpreting the sarcastic intentions of an author more than to predicting the sarcasm perception by others.
%R 10.18653/v1/2021.wnut-1.12
%U https://aclanthology.org/2021.wnut-1.12
%U https://doi.org/10.18653/v1/2021.wnut-1.12
%P 97-105

Markdown (Informal)

[Perceived and Intended Sarcasm Detection with Graph Attention Networks](https://aclanthology.org/2021.wnut-1.12) (Plepi & Flek, WNUT 2021)

• Perceived and Intended Sarcasm Detection with Graph Attention Networks (Plepi & Flek, WNUT 2021)

ACL

• Joan Plepi and Lucie Flek. 2021. Perceived and Intended Sarcasm Detection with Graph Attention Networks. In Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021), pages 97–105, Online. Association for Computational Linguistics.