An Empirical Study of Consistency Regularization for End-to-End Speech-to-Text Translation
Pengzhi Gao, Ruiqing Zhang, Zhongjun He, Hua Wu, Haifeng Wang
Abstract
Consistency regularization methods, such as R-Drop (Liang et al., 2021) and CrossConST (Gao et al., 2023), have achieved impressive supervised and zero-shot performance in the neural machine translation (NMT) field. Can we also boost end-to-end (E2E) speech-to-text translation (ST) by leveraging consistency regularization? In this paper, we conduct empirical studies on intra-modal and cross-modal consistency and propose two training strategies, SimRegCR and SimZeroCR, for E2E ST in regular and zero-shot scenarios. Experiments on the MuST-C benchmark show that our approaches achieve state-of-the-art (SOTA) performance in most translation directions. The analyses prove that regularization brought by the intra-modal consistency, instead of the modality gap, is crucial for the regular E2E ST, and the cross-modal consistency could close the modality gap and boost the zero-shot E2E ST performance.- Anthology ID:
- 2024.naacl-long.14
- 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:
- 242–256
- Language:
- URL:
- https://aclanthology.org/2024.naacl-long.14
- DOI:
- Cite (ACL):
- Pengzhi Gao, Ruiqing Zhang, Zhongjun He, Hua Wu, and Haifeng Wang. 2024. An Empirical Study of Consistency Regularization for End-to-End Speech-to-Text Translation. 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 242–256, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- An Empirical Study of Consistency Regularization for End-to-End Speech-to-Text Translation (Gao et al., NAACL 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.naacl-long.14.pdf