Contrastive and Consistency Learning for Neural Noisy-Channel Model in Spoken Language Understanding
Abstract
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 evaluation conditions. Therefore, a natural language understanding approach based on Automatic Speech Recognition (ASR) remains attractive because it can utilize a pre-trained general language model and adapt to the mismatch of the speech input environment. Using this module-based approach, we improve a noisy-channel model to handle transcription inconsistencies caused by ASR errors. We propose a two-stage method, Contrastive and Consistency Learning (CCL), that correlates error patterns between clean and noisy ASR transcripts and emphasizes the consistency of the latent features of the two transcripts. Experiments on four benchmark datasets show that CCL outperforms existing methods and improves the ASR robustness in various noisy environments. Code is available at https://github.com/syoung7388/CCL- Anthology ID:
- 2024.naacl-long.318
- Volume:
- Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Kevin Duh, Helena Gomez, Steven Bethard
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5698–5711
- Language:
- URL:
- https://aclanthology.org/2024.naacl-long.318
- DOI:
- 10.18653/v1/2024.naacl-long.318
- Cite (ACL):
- Suyoung Kim, Jiyeon Hwang, and Ho-Young Jung. 2024. Contrastive and Consistency Learning for Neural Noisy-Channel Model in Spoken Language Understanding. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 5698–5711, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Contrastive and Consistency Learning for Neural Noisy-Channel Model in Spoken Language Understanding (Kim et al., NAACL 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.naacl-long.318.pdf