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        <title>Lexical Simplification with the Deep Structured Similarity Model</title>
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    <name type="personal">
        <namePart type="given">Lis</namePart>
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        <namePart type="given">Xiaodong</namePart>
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            <title>Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)</title>
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            <namePart type="given">Greg</namePart>
            <namePart type="family">Kondrak</namePart>
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    <abstract>We explore the application of a Deep Structured Similarity Model (DSSM) to ranking in lexical simplification. Our results show that the DSSM can effectively capture fine-grained features to perform semantic matching when ranking substitution candidates, outperforming the state-of-the-art on two standard datasets used for the task.</abstract>
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%0 Conference Proceedings
%T Lexical Simplification with the Deep Structured Similarity Model
%A Pereira, Lis
%A Liu, Xiaodong
%A Lee, John
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F pereira-etal-2017-lexical
%X We explore the application of a Deep Structured Similarity Model (DSSM) to ranking in lexical simplification. Our results show that the DSSM can effectively capture fine-grained features to perform semantic matching when ranking substitution candidates, outperforming the state-of-the-art on two standard datasets used for the task.
%U https://aclanthology.org/I17-2073
%P 430-435

Markdown (Informal)

[Lexical Simplification with the Deep Structured Similarity Model](https://aclanthology.org/I17-2073) (Pereira et al., IJCNLP 2017)

• Lexical Simplification with the Deep Structured Similarity Model (Pereira et al., IJCNLP 2017)

ACL

• Lis Pereira, Xiaodong Liu, and John Lee. 2017. Lexical Simplification with the Deep Structured Similarity Model. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 430–435, Taipei, Taiwan. Asian Federation of Natural Language Processing.