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        <title>A Comparison of Context-sensitive Models for Lexical Substitution</title>
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    <name type="personal">
        <namePart type="given">Aina</namePart>
        <namePart type="family">Garí Soler</namePart>
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        <namePart type="given">Anne</namePart>
        <namePart type="family">Cocos</namePart>
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    <name type="personal">
        <namePart type="given">Marianna</namePart>
        <namePart type="family">Apidianaki</namePart>
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    <name type="personal">
        <namePart type="given">Chris</namePart>
        <namePart type="family">Callison-Burch</namePart>
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            <namePart type="given">Simon</namePart>
            <namePart type="family">Dobnik</namePart>
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        <name type="personal">
            <namePart type="given">Stergios</namePart>
            <namePart type="family">Chatzikyriakidis</namePart>
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        <name type="personal">
            <namePart type="given">Vera</namePart>
            <namePart type="family">Demberg</namePart>
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    <abstract>Word embedding representations provide good estimates of word meaning and give state-of-the art performance in semantic tasks. Embedding approaches differ as to whether and how they account for the context surrounding a word. We present a comparison of different word and context representations on the task of proposing substitutes for a target word in context (lexical substitution). We also experiment with tuning contextualized word embeddings on a dataset of sense-specific instances for each target word. We show that powerful contextualized word representations, which give high performance in several semantics-related tasks, deal less well with the subtle in-context similarity relationships needed for substitution. This is better handled by models trained with this objective in mind, where the inter-dependence between word and context representations is explicitly modeled during training.</abstract>
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    <identifier type="doi">10.18653/v1/W19-0423</identifier>
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        <date>2019-05</date>
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            <start>271</start>
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%0 Conference Proceedings
%T A Comparison of Context-sensitive Models for Lexical Substitution
%A Garí Soler, Aina
%A Cocos, Anne
%A Apidianaki, Marianna
%A Callison-Burch, Chris
%Y Dobnik, Simon
%Y Chatzikyriakidis, Stergios
%Y Demberg, Vera
%S Proceedings of the 13th International Conference on Computational Semantics - Long Papers
%D 2019
%8 May
%I Association for Computational Linguistics
%C Gothenburg, Sweden
%F gari-soler-etal-2019-comparison
%X Word embedding representations provide good estimates of word meaning and give state-of-the art performance in semantic tasks. Embedding approaches differ as to whether and how they account for the context surrounding a word. We present a comparison of different word and context representations on the task of proposing substitutes for a target word in context (lexical substitution). We also experiment with tuning contextualized word embeddings on a dataset of sense-specific instances for each target word. We show that powerful contextualized word representations, which give high performance in several semantics-related tasks, deal less well with the subtle in-context similarity relationships needed for substitution. This is better handled by models trained with this objective in mind, where the inter-dependence between word and context representations is explicitly modeled during training.
%R 10.18653/v1/W19-0423
%U https://aclanthology.org/W19-0423
%U https://doi.org/10.18653/v1/W19-0423
%P 271-282

Markdown (Informal)

[A Comparison of Context-sensitive Models for Lexical Substitution](https://aclanthology.org/W19-0423) (Garí Soler et al., IWCS 2019)

• A Comparison of Context-sensitive Models for Lexical Substitution (Garí Soler et al., IWCS 2019)

ACL

• Aina Garí Soler, Anne Cocos, Marianna Apidianaki, and Chris Callison-Burch. 2019. A Comparison of Context-sensitive Models for Lexical Substitution. In Proceedings of the 13th International Conference on Computational Semantics - Long Papers, pages 271–282, Gothenburg, Sweden. Association for Computational Linguistics.