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.

通过互相学习和大间距对比学习，提出一种新的框架 ML-LMCL 来提高自动语音识别在对话系统中口语理解的鲁棒性。通过在手动转录和自动识别转录之间共享知识，引入距离极化正则化器以尽量避免推开簇内成对样本，使用周期性退火调度缓解 KL 关于消失问题，并在三个数据集上表明 ML-LMCL 模型胜过现有模型并获得最新的性能。