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        <title>Modality Enriched Neural Network for Metaphor Detection</title>
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        <namePart type="given">Mingyu</namePart>
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            <title>Proceedings of the 28th International Conference on Computational Linguistics</title>
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            <namePart type="given">Donia</namePart>
            <namePart type="family">Scott</namePart>
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        <name type="personal">
            <namePart type="given">Nuria</namePart>
            <namePart type="family">Bel</namePart>
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        <name type="personal">
            <namePart type="given">Chengqing</namePart>
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    <abstract>Metaphor as a cognitive mechanism in human’s conceptual system manifests itself an effective way for language communication. Although being intuitively sensible for human, metaphor detection is still a challenging task due to the subtle ontological differences between metaphorical and non-metaphorical expressions. This work proposes a modality enriched deep learning model for tackling this unsolved issue. It provides a new perspective for understanding metaphor as a modality shift, as in ‘sweet voice’. It also attempts to enhance metaphor detection by combining deep learning with effective linguistic insight. Extending the work at Wan et al. (2020), we concatenate word sensorimotor scores (Lynott et al., 2019) with word vectors as the input of attention-based Bi-LSTM using a benchmark dataset–the VUA corpus. The experimental results show great F1 improvement (above 0.5%) of the proposed model over other methods in record, demonstrating the usefulness of leveraging modality norms for metaphor detection.</abstract>
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    <identifier type="doi">10.18653/v1/2020.coling-main.270</identifier>
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%0 Conference Proceedings
%T Modality Enriched Neural Network for Metaphor Detection
%A Wan, Mingyu
%A Xing, Baixi
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F wan-xing-2020-modality
%X Metaphor as a cognitive mechanism in human’s conceptual system manifests itself an effective way for language communication. Although being intuitively sensible for human, metaphor detection is still a challenging task due to the subtle ontological differences between metaphorical and non-metaphorical expressions. This work proposes a modality enriched deep learning model for tackling this unsolved issue. It provides a new perspective for understanding metaphor as a modality shift, as in ‘sweet voice’. It also attempts to enhance metaphor detection by combining deep learning with effective linguistic insight. Extending the work at Wan et al. (2020), we concatenate word sensorimotor scores (Lynott et al., 2019) with word vectors as the input of attention-based Bi-LSTM using a benchmark dataset–the VUA corpus. The experimental results show great F1 improvement (above 0.5%) of the proposed model over other methods in record, demonstrating the usefulness of leveraging modality norms for metaphor detection.
%R 10.18653/v1/2020.coling-main.270
%U https://aclanthology.org/2020.coling-main.270
%U https://doi.org/10.18653/v1/2020.coling-main.270
%P 3036-3042

Markdown (Informal)

[Modality Enriched Neural Network for Metaphor Detection](https://aclanthology.org/2020.coling-main.270) (Wan & Xing, COLING 2020)

• Modality Enriched Neural Network for Metaphor Detection (Wan & Xing, COLING 2020)

ACL

• Mingyu Wan and Baixi Xing. 2020. Modality Enriched Neural Network for Metaphor Detection. In Proceedings of the 28th International Conference on Computational Linguistics, pages 3036–3042, Barcelona, Spain (Online). International Committee on Computational Linguistics.