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    <abstract>Pretrained language models (PLMs) trained on large-scale unlabeled corpus are typically fine-tuned 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 fine-tuning fail to efficiently capture task-specific patterns, especially in 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.</abstract>
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%0 Conference Proceedings
%T Task-guided Disentangled Tuning for Pretrained Language Models
%A Zeng, Jiali
%A Jiang, Yufan
%A Wu, Shuangzhi
%A Yin, Yongjing
%A Li, Mu
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Findings of the Association for Computational Linguistics: ACL 2022
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F zeng-etal-2022-task
%X Pretrained language models (PLMs) trained on large-scale unlabeled corpus are typically fine-tuned 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 fine-tuning fail to efficiently capture task-specific patterns, especially in 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.
%R 10.18653/v1/2022.findings-acl.247
%U https://aclanthology.org/2022.findings-acl.247
%U https://doi.org/10.18653/v1/2022.findings-acl.247
%P 3126-3137

Markdown (Informal)

[Task-guided Disentangled Tuning for Pretrained Language Models](https://aclanthology.org/2022.findings-acl.247) (Zeng et al., Findings 2022)

• Task-guided Disentangled Tuning for Pretrained Language Models (Zeng et al., Findings 2022)

ACL

• Jiali Zeng, Yufan Jiang, Shuangzhi Wu, Yongjing Yin, and Mu Li. 2022. Task-guided Disentangled Tuning for Pretrained Language Models. In Findings of the Association for Computational Linguistics: ACL 2022, pages 3126–3137, Dublin, Ireland. Association for Computational Linguistics.