Task-guided Disentangled Tuning for Pretrained Language Models
Jiali Zeng, Yufan Jiang, Shuangzhi Wu, Yongjing Yin, Mu Li · Findings of the Association for Computational Linguistics: ACL 2022 · 2022
Pretrained language models (PLMs) trained on large-scale unlabeled corpus are typically finetuned on task-specific downstream datasets, which have produced state-of-the-art results on various NLP tasks.However, the data discrepancy issue in domain and scale makes finetuning fail to efficiently capture task-specific patterns, especially in the low data regime.To address this issue, we propose Task-guided Disentangled Tuning (TDT) for PLMs, which enhances the generalization of representations by disentangling task-relevant signals from the entangled representations.For a given task, we introduce a learnable confidence model to detect indicative guidance from context, and further propose a disentangled regularization to mitigate the over-reliance problem.Experimental results on GLUE and CLUE benchmarks show that TDT gives consistently better results than fine-tuning with different PLMs, and extensive analysis demonstrates the effectiveness and robustness of our method.Code is available at https://github.com/lemon0830/TDT.