AESOP: Paraphrase Generation with Adaptive Syntactic Control

AESOP: Paraphrase Generation with Adaptive Syntactic Control

Jiao Sun, Xuezhe Ma, and Nanyun Peng, in The 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021.

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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.

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Bib Entry

@inproceedings{sun2021aesop,
  title = {AESOP: Paraphrase Generation with Adaptive Syntactic Control},
  author = {Sun, Jiao and Ma, Xuezhe and Peng, Nanyun},
  booktitle = {The 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2021}
}

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  @inproceedings{spangher2022sequentially,
    title = {Sequentially Controlled Text Generation},
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• AESOP: Paraphrase Generation with Adaptive Syntactic Control

  Jiao Sun, Xuezhe Ma, and Nanyun Peng, in The 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021.
  Full Text Code Abstract BibTeX Details

  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.

  @inproceedings{sun2021aesop,
    title = {AESOP: Paraphrase Generation with Adaptive Syntactic Control},
    author = {Sun, Jiao and Ma, Xuezhe and Peng, Nanyun},
    booktitle = {The 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
    year = {2021}
  }
  

  Details