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        <title>Neural Syntactic Preordering for Controlled Paraphrase Generation</title>
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    <name type="personal">
        <namePart type="given">Tanya</namePart>
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        <name type="personal">
            <namePart type="given">Dan</namePart>
            <namePart type="family">Jurafsky</namePart>
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    <abstract>Paraphrasing natural language sentences is a multifaceted process: it might involve replacing individual words or short phrases, local rearrangement of content, or high-level restructuring like topicalization or passivization. Past approaches struggle to cover this space of paraphrase possibilities in an interpretable manner. Our work, inspired by pre-ordering literature in machine translation, uses syntactic transformations to softly “reorder” the source sentence and guide our neural paraphrasing model. First, given an input sentence, we derive a set of feasible syntactic rearrangements using an encoder-decoder model. This model operates over a partially lexical, partially syntactic view of the sentence and can reorder big chunks. Next, we use each proposed rearrangement to produce a sequence of position embeddings, which encourages our final encoder-decoder paraphrase model to attend to the source words in a particular order. Our evaluation, both automatic and human, shows that the proposed system retains the quality of the baseline approaches while giving a substantial increase in the diversity of the generated paraphrases.</abstract>
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%0 Conference Proceedings
%T Neural Syntactic Preordering for Controlled Paraphrase Generation
%A Goyal, Tanya
%A Durrett, Greg
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F goyal-durrett-2020-neural
%X Paraphrasing natural language sentences is a multifaceted process: it might involve replacing individual words or short phrases, local rearrangement of content, or high-level restructuring like topicalization or passivization. Past approaches struggle to cover this space of paraphrase possibilities in an interpretable manner. Our work, inspired by pre-ordering literature in machine translation, uses syntactic transformations to softly “reorder” the source sentence and guide our neural paraphrasing model. First, given an input sentence, we derive a set of feasible syntactic rearrangements using an encoder-decoder model. This model operates over a partially lexical, partially syntactic view of the sentence and can reorder big chunks. Next, we use each proposed rearrangement to produce a sequence of position embeddings, which encourages our final encoder-decoder paraphrase model to attend to the source words in a particular order. Our evaluation, both automatic and human, shows that the proposed system retains the quality of the baseline approaches while giving a substantial increase in the diversity of the generated paraphrases.
%R 10.18653/v1/2020.acl-main.22
%U https://aclanthology.org/2020.acl-main.22/
%U https://doi.org/10.18653/v1/2020.acl-main.22
%P 238-252

Markdown (Informal)

[Neural Syntactic Preordering for Controlled Paraphrase Generation](https://aclanthology.org/2020.acl-main.22/) (Goyal & Durrett, ACL 2020)

• Neural Syntactic Preordering for Controlled Paraphrase Generation (Goyal & Durrett, ACL 2020)

ACL

• Tanya Goyal and Greg Durrett. 2020. Neural Syntactic Preordering for Controlled Paraphrase Generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 238–252, Online. Association for Computational Linguistics.