  Spoken language understanding (SLU) is an essential task for machines to
understand human speech for better interactions. However, errors from the
automatic speech recognizer (ASR) usually hurt the understanding performance.
In reality, ASR systems may not be easy to adjust for the target scenarios.
Therefore, this paper focuses on learning utterance representations that are
robust to ASR errors using a contrastive objective, and further strengthens the
generalization ability by combining supervised contrastive learning and
self-distillation in model fine-tuning. Experiments on three benchmark datasets
demonstrate the effectiveness of our proposed approach.

Computation and Language;Sound;Audio and Speech Processing