ML-LMCL: Mutual Learning and Large-Margin Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding

• URL: http://arxiv.org/abs/2311.11375v1
• Date: Sun, 19 Nov 2023 16:53:35 GMT
• Title: ML-LMCL: Mutual Learning and Large-Margin Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding
• Authors: Xuxin Cheng, Bowen Cao, Qichen Ye, Zhihong Zhu, Hongxiang Li, Yuexian Zou
• Abstract summary: We propose Mutual Learning and Large-Margin Contrastive Learning (ML-LMCL) to improve automatic speech recognition (ASR) robustness. In fine-tuning, we apply mutual learning and train two SLU models on the manual transcripts and the ASR transcripts, respectively. Experiments on three datasets show that ML-LMCL outperforms existing models and achieves new state-of-the-art performance.
• Score: 55.39105863825107
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spoken language understanding (SLU) is a fundamental task in the task-oriented dialogue systems. However, the inevitable errors from automatic speech recognition (ASR) usually impair the understanding performance and lead to error propagation. Although there are some attempts to address this problem through contrastive learning, they (1) treat clean manual transcripts and ASR transcripts equally without discrimination in fine-tuning; (2) neglect the fact that the semantically similar pairs are still pushed away when applying contrastive learning; (3) suffer from the problem of Kullback-Leibler (KL) vanishing. In this paper, we propose Mutual Learning and Large-Margin Contrastive Learning (ML-LMCL), a novel framework for improving ASR robustness in SLU. Specifically, in fine-tuning, we apply mutual learning and train two SLU models on the manual transcripts and the ASR transcripts, respectively, aiming to iteratively share knowledge between these two models. We also introduce a distance polarization regularizer to avoid pushing away the intra-cluster pairs as much as possible. Moreover, we use a cyclical annealing schedule to mitigate KL vanishing issue. Experiments on three datasets show that ML-LMCL outperforms existing models and achieves new state-of-the-art performance.

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