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- W4378715642 abstract "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." @default.
- W4378715642 created "2023-05-30" @default.
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- W4378715642 date "2023-05-26" @default.
- W4378715642 modified "2023-09-27" @default.
- W4378715642 title "PIP: Parse-Instructed Prefix for Syntactically Controlled Paraphrase Generation" @default.
- W4378715642 doi "https://doi.org/10.48550/arxiv.2305.16701" @default.
- W4378715642 hasPublicationYear "2023" @default.
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