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        <title>PIP: Parse-Instructed Prefix for Syntactically Controlled Paraphrase Generation</title>
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        <namePart type="given">Yixin</namePart>
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        <namePart type="given">Kuan-Hao</namePart>
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    <abstract>Syntactically controlled paraphrase generation requires language models to generate paraphrases for sentences according to specific syntactic structures. Existing fine-tuning methods on this task is costly, as all 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). Comparing to traditional fine-tuning methods for this task, PIP is a compute-efficient alternative with 10 times less learnable parameters. Comparing to existing prefix-tuning methods, PIP excels at capturing syntax control information, achieving significantly higher performance at the same level of learnable parameter count.</abstract>
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%0 Conference Proceedings
%T PIP: Parse-Instructed Prefix for Syntactically Controlled Paraphrase Generation
%A Wan, Yixin
%A Huang, Kuan-Hao
%A Chang, Kai-Wei
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F wan-etal-2023-pip
%X Syntactically controlled paraphrase generation requires language models to generate paraphrases for sentences according to specific syntactic structures. Existing fine-tuning methods on this task is costly, as all 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). Comparing to traditional fine-tuning methods for this task, PIP is a compute-efficient alternative with 10 times less learnable parameters. Comparing to existing prefix-tuning methods, PIP excels at capturing syntax control information, achieving significantly higher performance at the same level of learnable parameter count.
%R 10.18653/v1/2023.findings-acl.659
%U https://aclanthology.org/2023.findings-acl.659
%U https://doi.org/10.18653/v1/2023.findings-acl.659
%P 10372-10380

Markdown (Informal)

[PIP: Parse-Instructed Prefix for Syntactically Controlled Paraphrase Generation](https://aclanthology.org/2023.findings-acl.659) (Wan et al., Findings 2023)

• PIP: Parse-Instructed Prefix for Syntactically Controlled Paraphrase Generation (Wan et al., Findings 2023)

ACL

• Yixin Wan, Kuan-Hao Huang, and Kai-Wei Chang. 2023. PIP: Parse-Instructed Prefix for Syntactically Controlled Paraphrase Generation. In Findings of the Association for Computational Linguistics: ACL 2023, pages 10372–10380, Toronto, Canada. Association for Computational Linguistics.