An Information-theoretic Multi-task Representation Learning Framework for Natural Language Understanding

• URL: http://arxiv.org/abs/2503.04667v1
• Date: Thu, 06 Mar 2025 17:59:51 GMT
• Title: An Information-theoretic Multi-task Representation Learning Framework for Natural Language Understanding
• Authors: Dou Hu, Lingwei Wei, Wei Zhou, Songlin Hu,
• Abstract summary: This paper proposes a new principled multi-task representation learning framework (InfoMTL)<n>It ensures sufficiency of shared representations for all tasks and mitigates the negative effect of redundant features.<n>It can enhance language understanding of pre-trained language models (PLMs) under the multi-task paradigm.
• Score: 29.36409607847339
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes a new principled multi-task representation learning framework (InfoMTL) to extract noise-invariant sufficient representations for all tasks. It ensures sufficiency of shared representations for all tasks and mitigates the negative effect of redundant features, which can enhance language understanding of pre-trained language models (PLMs) under the multi-task paradigm. Firstly, a shared information maximization principle is proposed to learn more sufficient shared representations for all target tasks. It can avoid the insufficiency issue arising from representation compression in the multi-task paradigm. Secondly, a task-specific information minimization principle is designed to mitigate the negative effect of potential redundant features in the input for each task. It can compress task-irrelevant redundant information and preserve necessary information relevant to the target for multi-task prediction. Experiments on six classification benchmarks show that our method outperforms 12 comparative multi-task methods under the same multi-task settings, especially in data-constrained and noisy scenarios. Extensive experiments demonstrate that the learned representations are more sufficient, data-efficient, and robust.

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