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PIP: Parse-Instructed Prefix for Syntactically Controlled Paraphrase Generation

Yixin Wan, Kuan-Hao Huang, and Kai-Wei Chang, in ACL-Finding (short), 2023.

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Syntactically controlled paraphrase generation requires language models to generate paraphrases for sentences according to specific syntactic structures. Existing fine-tuning methods for this task are costly as all the parameters of the model need to be updated during the training process. Inspired by recent studies on parameter-efficient learning, we propose Parse-Instructed Prefix (PIP), a novel adaptation of prefix-tuning to tune large pre-trained language models on syntactically controlled paraphrase generation task in a low-data setting with significantly less training cost. We introduce two methods to instruct a model’s encoder prefix to capture syntax-related knowledge: direct initiation (PIP-Direct) and indirect optimization (PIP-Indirect). In contrast to traditional fine-tuning methods for this task, PIP is a compute-efficient alternative with 10 times less learnable parameters. Compared to existing prefix-tuning methods, PIP excels at capturing syntax control information, achieving significantly higher performance at the same level of learnable parameter count.

Bib Entry

  author = {Wan, Yixin and Huang, Kuan-Hao and Chang, Kai-Wei},
  title = {PIP: Parse-Instructed Prefix for Syntactically Controlled Paraphrase Generation},
  booktitle = {ACL-Finding (short)},
  presentation_id = {},
  year = {2023}

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