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        <title>AESOP: Paraphrase Generation with Adaptive Syntactic Control</title>
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    <name type="personal">
        <namePart type="given">Jiao</namePart>
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        <namePart type="given">Xuezhe</namePart>
        <namePart type="family">Ma</namePart>
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    <name type="personal">
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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    <abstract>We propose to control paraphrase generation through carefully chosen target syntactic structures to generate more proper and higher quality paraphrases. Our model, AESOP, leverages a pretrained language model and adds deliberately chosen syntactical control via a retrieval-based selection module to generate fluent paraphrases. Experiments show that AESOP achieves state-of-the-art performances on semantic preservation and syntactic conformation on two benchmark datasets with ground-truth syntactic control from human-annotated exemplars. Moreover, with the retrieval-based target syntax selection module, AESOP generates paraphrases with even better qualities than the current best model using human-annotated target syntactic parses according to human evaluation. We further demonstrate the effectiveness of AESOP to improve classification models’ robustness to syntactic perturbation by data augmentation on two GLUE tasks.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.420</identifier>
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        <date>2021-11</date>
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%0 Conference Proceedings
%T AESOP: Paraphrase Generation with Adaptive Syntactic Control
%A Sun, Jiao
%A Ma, Xuezhe
%A Peng, Nanyun
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F sun-etal-2021-aesop
%X We propose to control paraphrase generation through carefully chosen target syntactic structures to generate more proper and higher quality paraphrases. Our model, AESOP, leverages a pretrained language model and adds deliberately chosen syntactical control via a retrieval-based selection module to generate fluent paraphrases. Experiments show that AESOP achieves state-of-the-art performances on semantic preservation and syntactic conformation on two benchmark datasets with ground-truth syntactic control from human-annotated exemplars. Moreover, with the retrieval-based target syntax selection module, AESOP generates paraphrases with even better qualities than the current best model using human-annotated target syntactic parses according to human evaluation. We further demonstrate the effectiveness of AESOP to improve classification models’ robustness to syntactic perturbation by data augmentation on two GLUE tasks.
%R 10.18653/v1/2021.emnlp-main.420
%U https://aclanthology.org/2021.emnlp-main.420
%U https://doi.org/10.18653/v1/2021.emnlp-main.420
%P 5176-5189

Markdown (Informal)

[AESOP: Paraphrase Generation with Adaptive Syntactic Control](https://aclanthology.org/2021.emnlp-main.420) (Sun et al., EMNLP 2021)

• AESOP: Paraphrase Generation with Adaptive Syntactic Control (Sun et al., EMNLP 2021)

ACL

• Jiao Sun, Xuezhe Ma, and Nanyun Peng. 2021. AESOP: Paraphrase Generation with Adaptive Syntactic Control. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 5176–5189, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.