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        <title>Killing Four Birds with Two Stones: Multi-Task Learning for Non-Literal Language Detection</title>
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    <name type="personal">
        <namePart type="given">Erik-Lân</namePart>
        <namePart type="family">Do Dinh</namePart>
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        <namePart type="given">Steffen</namePart>
        <namePart type="family">Eger</namePart>
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    <name type="personal">
        <namePart type="given">Iryna</namePart>
        <namePart type="family">Gurevych</namePart>
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    <abstract>Non-literal language phenomena such as idioms or metaphors are commonly studied in isolation from each other in NLP. However, often similar definitions and features are being used for different phenomena, challenging the distinction. Instead, we propose to view the detection problem as a generalized non-literal language classification problem. In this paper we investigate multi-task learning for related non-literal language phenomena. We show that in contrast to simply joining the data of multiple tasks, multi-task learning consistently improves upon four metaphor and idiom detection tasks in two languages, English and German. Comparing two state-of-the-art multi-task learning architectures, we also investigate when soft parameter sharing and learned information flow can be beneficial for our related tasks. We make our adapted code publicly available.</abstract>
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        <url>https://aclanthology.org/C18-1132</url>
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%0 Conference Proceedings
%T Killing Four Birds with Two Stones: Multi-Task Learning for Non-Literal Language Detection
%A Do Dinh, Erik-Lân
%A Eger, Steffen
%A Gurevych, Iryna
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 aug
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F do-dinh-etal-2018-killing
%X Non-literal language phenomena such as idioms or metaphors are commonly studied in isolation from each other in NLP. However, often similar definitions and features are being used for different phenomena, challenging the distinction. Instead, we propose to view the detection problem as a generalized non-literal language classification problem. In this paper we investigate multi-task learning for related non-literal language phenomena. We show that in contrast to simply joining the data of multiple tasks, multi-task learning consistently improves upon four metaphor and idiom detection tasks in two languages, English and German. Comparing two state-of-the-art multi-task learning architectures, we also investigate when soft parameter sharing and learned information flow can be beneficial for our related tasks. We make our adapted code publicly available.
%U https://aclanthology.org/C18-1132
%P 1558-1569

Markdown (Informal)

[Killing Four Birds with Two Stones: Multi-Task Learning for Non-Literal Language Detection](https://aclanthology.org/C18-1132) (Do Dinh et al., COLING 2018)

• Killing Four Birds with Two Stones: Multi-Task Learning for Non-Literal Language Detection (Do Dinh et al., COLING 2018)

ACL

• Erik-Lân Do Dinh, Steffen Eger, and Iryna Gurevych. 2018. Killing Four Birds with Two Stones: Multi-Task Learning for Non-Literal Language Detection. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1558–1569, Santa Fe, New Mexico, USA. Association for Computational Linguistics.