Abstract
This paper describes Edinburgh’s submissions to the IWSLT2021 multilingual speech translation (ST) task. We aim at improving multilingual translation and zero-shot performance in the constrained setting (without using any extra training data) through methods that encourage transfer learning and larger capacity modeling with advanced neural components. We build our end-to-end multilingual ST model based on Transformer, integrating techniques including adaptive speech feature selection, language-specific modeling, multi-task learning, deep and big Transformer, sparsified linear attention and root mean square layer normalization. We adopt data augmentation using machine translation models for ST which converts the zero-shot problem into a zero-resource one. Experimental results show that these methods deliver substantial improvements, surpassing the official baseline by > 15 average BLEU and outperforming our cascading system by > 2 average BLEU. Our final submission achieves competitive performance (runner up).- Anthology ID:
- 2021.iwslt-1.19
- Volume:
- Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021)
- Month:
- August
- Year:
- 2021
- Address:
- Bangkok, Thailand (online)
- Editors:
- Marcello Federico, Alex Waibel, Marta R. Costa-jussà, Jan Niehues, Sebastian Stuker, Elizabeth Salesky
- Venue:
- IWSLT
- SIG:
- SIGSLT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 160–168
- Language:
- URL:
- https://aclanthology.org/2021.iwslt-1.19
- DOI:
- 10.18653/v1/2021.iwslt-1.19
- Cite (ACL):
- Biao Zhang and Rico Sennrich. 2021. Edinburgh’s End-to-End Multilingual Speech Translation System for IWSLT 2021. In Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021), pages 160–168, Bangkok, Thailand (online). Association for Computational Linguistics.
- Cite (Informal):
- Edinburgh’s End-to-End Multilingual Speech Translation System for IWSLT 2021 (Zhang & Sennrich, IWSLT 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.iwslt-1.19.pdf