PIP: Parse-Instructed Prefix for Syntactically Controlled Paraphrase
  Generation
Yixin Wan Kuan-Hao Huang Kai-Wei Chang
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.