CMU’s IWSLT 2022 Dialect Speech Translation System

Brian Yan, Patrick Fernandes, Siddharth Dalmia, Jiatong Shi, Yifan Peng, Dan Berrebbi, Xinyi Wang, Graham Neubig, Shinji Watanabe


Abstract
This paper describes CMU’s submissions to the IWSLT 2022 dialect speech translation (ST) shared task for translating Tunisian-Arabic speech to English text. We use additional paired Modern Standard Arabic data (MSA) to directly improve the speech recognition (ASR) and machine translation (MT) components of our cascaded systems. We also augment the paired ASR data with pseudo translations via sequence-level knowledge distillation from an MT model and use these artificial triplet ST data to improve our end-to-end (E2E) systems. Our E2E models are based on the Multi-Decoder architecture with searchable hidden intermediates. We extend the Multi-Decoder by orienting the speech encoder towards the target language by applying ST supervision as hierarchical connectionist temporal classification (CTC) multi-task. During inference, we apply joint decoding of the ST CTC and ST autoregressive decoder branches of our modified Multi-Decoder. Finally, we apply ROVER voting, posterior combination, and minimum bayes-risk decoding with combined N-best lists to ensemble our various cascaded and E2E systems. Our best systems reached 20.8 and 19.5 BLEU on test2 (blind) and test1 respectively. Without any additional MSA data, we reached 20.4 and 19.2 on the same test sets.
Anthology ID:
2022.iwslt-1.27
Volume:
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)
Month:
May
Year:
2022
Address:
Dublin, Ireland (in-person and online)
Venues:
ACL | IWSLT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
298–307
Language:
URL:
https://aclanthology.org/2022.iwslt-1.27
DOI:
10.18653/v1/2022.iwslt-1.27
Bibkey:
Cite (ACL):
Brian Yan, Patrick Fernandes, Siddharth Dalmia, Jiatong Shi, Yifan Peng, Dan Berrebbi, Xinyi Wang, Graham Neubig, and Shinji Watanabe. 2022. CMU’s IWSLT 2022 Dialect Speech Translation System. In Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022), pages 298–307, Dublin, Ireland (in-person and online). Association for Computational Linguistics.
Cite (Informal):
CMU’s IWSLT 2022 Dialect Speech Translation System (Yan et al., IWSLT 2022)
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PDF:
https://aclanthology.org/2022.iwslt-1.27.pdf