ML-LMCL：用于提升面向任务的对话系统中口语理解ASR鲁棒性的互学习与大间隔对比学习
ML-LMCL: Mutual Learning and Large-Margin Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding
AI总结：
针对ASR错误影响口语理解性能的问题，提出ML-LMCL框架，通过互学习在手动与ASR转录文本间共享知识，引入距离极化正则化器避免推远相似对，并采用循环退火调度缓解KL消失，在三个数据集上达到最先进性能。
摘要：
口语理解（SLU）是面向任务的对话系统中的一项基础任务。然而，来自自动语音识别（ASR）的不可避免错误通常会损害理解性能并导致错误传播。尽管已有一些通过对比学习解决此问题的尝试，但它们（1）在微调时无差别地同等对待干净的手动转录文本和ASR转录文本；（2）忽略了在应用对比学习时语义相似对仍被推远的事实；（3）面临Kullback-Leibler（KL）消失问题。在本文中，我们提出了互学习与大间隔对比学习（ML-LMCL），这是一个用于提升SLU中ASR鲁棒性的新框架。具体而言，在微调中，我们应用互学习并分别在手动转录文本和ASR转录文本上训练两个SLU模型，旨在迭代地在这两个模型间共享知识。我们还引入了距离极化正则化器，以尽可能避免推远簇内对。此外，我们使用循环退火调度来缓解KL消失问题。在三个数据集上的实验表明，ML-LMCL优于现有模型并取得了新的最先进性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
