2024
Contrastive and Consistency Learning for Neural Noisy-Channel Model in Spoken Language Understanding
NAACL 2024long
Recently, deep end-to-end learning has been studied for intent classification in Spoken Language Understanding (SLU). However, end-to-end models require a large amount of speech data with intent labels, and highly optimized models are generally sensitive to the inconsistency between the training and…