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        <title>Learning distributed event representations with a multi-task approach</title>
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        <namePart type="given">Xudong</namePart>
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        <namePart type="given">Asad</namePart>
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    <name type="personal">
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            <title>Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics</title>
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            <namePart type="given">Malvina</namePart>
            <namePart type="family">Nissim</namePart>
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        <name type="personal">
            <namePart type="given">Jonathan</namePart>
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        <name type="personal">
            <namePart type="given">Alessandro</namePart>
            <namePart type="family">Lenci</namePart>
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    <abstract>Human world knowledge contains information about prototypical events and their participants and locations. In this paper, we train the first models using multi-task learning that can both predict missing event participants and also perform semantic role classification based on semantic plausibility. Our best-performing model is an improvement over the previous state-of-the-art on thematic fit modelling tasks. The event embeddings learned by the model can additionally be used effectively in an event similarity task, also outperforming the state-of-the-art.</abstract>
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    <identifier type="doi">10.18653/v1/S18-2002</identifier>
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%0 Conference Proceedings
%T Learning distributed event representations with a multi-task approach
%A Hong, Xudong
%A Sayeed, Asad
%A Demberg, Vera
%Y Nissim, Malvina
%Y Berant, Jonathan
%Y Lenci, Alessandro
%S Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F hong-etal-2018-learning
%X Human world knowledge contains information about prototypical events and their participants and locations. In this paper, we train the first models using multi-task learning that can both predict missing event participants and also perform semantic role classification based on semantic plausibility. Our best-performing model is an improvement over the previous state-of-the-art on thematic fit modelling tasks. The event embeddings learned by the model can additionally be used effectively in an event similarity task, also outperforming the state-of-the-art.
%R 10.18653/v1/S18-2002
%U https://aclanthology.org/S18-2002/
%U https://doi.org/10.18653/v1/S18-2002
%P 11-21

Markdown (Informal)

[Learning distributed event representations with a multi-task approach](https://aclanthology.org/S18-2002/) (Hong et al., *SEM 2018)

• Learning distributed event representations with a multi-task approach (Hong et al., *SEM 2018)

ACL

• Xudong Hong, Asad Sayeed, and Vera Demberg. 2018. Learning distributed event representations with a multi-task approach. In Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, pages 11–21, New Orleans, Louisiana. Association for Computational Linguistics.