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        <title>Simple and Effective Text Simplification Using Semantic and Neural Methods</title>
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        <namePart type="given">Elior</namePart>
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            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
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        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
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    <abstract>Sentence splitting is a major simplification operator. Here we present a simple and efficient splitting algorithm based on an automatic semantic parser. After splitting, the text is amenable for further fine-tuned simplification operations. In particular, we show that neural Machine Translation can be effectively used in this situation. Previous application of Machine Translation for simplification suffers from a considerable disadvantage in that they are over-conservative, often failing to modify the source in any way. Splitting based on semantic parsing, as proposed here, alleviates this issue. Extensive automatic and human evaluation shows that the proposed method compares favorably to the state-of-the-art in combined lexical and structural simplification.</abstract>
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    <identifier type="doi">10.18653/v1/P18-1016</identifier>
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%0 Conference Proceedings
%T Simple and Effective Text Simplification Using Semantic and Neural Methods
%A Sulem, Elior
%A Abend, Omri
%A Rappoport, Ari
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F sulem-etal-2018-simple
%X Sentence splitting is a major simplification operator. Here we present a simple and efficient splitting algorithm based on an automatic semantic parser. After splitting, the text is amenable for further fine-tuned simplification operations. In particular, we show that neural Machine Translation can be effectively used in this situation. Previous application of Machine Translation for simplification suffers from a considerable disadvantage in that they are over-conservative, often failing to modify the source in any way. Splitting based on semantic parsing, as proposed here, alleviates this issue. Extensive automatic and human evaluation shows that the proposed method compares favorably to the state-of-the-art in combined lexical and structural simplification.
%R 10.18653/v1/P18-1016
%U https://aclanthology.org/P18-1016
%U https://doi.org/10.18653/v1/P18-1016
%P 162-173

Markdown (Informal)

[Simple and Effective Text Simplification Using Semantic and Neural Methods](https://aclanthology.org/P18-1016) (Sulem et al., ACL 2018)

• Simple and Effective Text Simplification Using Semantic and Neural Methods (Sulem et al., ACL 2018)

ACL

• Elior Sulem, Omri Abend, and Ari Rappoport. 2018. Simple and Effective Text Simplification Using Semantic and Neural Methods. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 162–173, Melbourne, Australia. Association for Computational Linguistics.