Abstract
Pretraining and multitask learning are widely used to improve the speech translation performance. In this study, we are interested in training a speech translation model along with an auxiliary text translation task. We conduct a detailed analysis to understand the impact of the auxiliary task on the primary task within the multitask learning framework. Our analysis confirms that multitask learning tends to generate similar decoder representations from different modalities and preserve more information from the pretrained text translation modules. We observe minimal negative transfer effect between the two tasks and sharing more parameters is helpful to transfer knowledge from the text task to the speech task. The analysis also reveals that the modality representation difference at the top decoder layers is still not negligible, and those layers are critical for the translation quality. Inspired by these findings, we propose three methods to improve translation quality. First, a parameter sharing and initialization strategy is proposed to enhance information sharing between the tasks. Second, a novel attention-based regularization is proposed for the encoders and pulls the representations from different modalities closer. Third, an online knowledge distillation is proposed to enhance the knowledge transfer from the text to the speech task. Our experiments show that the proposed approach improves translation performance by more than 2 BLEU over a strong baseline and achieves state-of-the-art results on the MuST-C English-German, English-French and English-Spanish language pairs.- Anthology ID:
- 2021.acl-long.328
- Volume:
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Venues:
- ACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4252–4261
- Language:
- URL:
- https://aclanthology.org/2021.acl-long.328
- DOI:
- 10.18653/v1/2021.acl-long.328
- Cite (ACL):
- Yun Tang, Juan Pino, Xian Li, Changhan Wang, and Dmitriy Genzel. 2021. Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Task. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 4252–4261, Online. Association for Computational Linguistics.
- Cite (Informal):
- Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Task (Tang et al., ACL-IJCNLP 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.acl-long.328.pdf
- Data
- MuST-C