The NiuTrans End-to-End Speech Translation System for IWSLT 2021 Offline Task

Chen Xu, Xiaoqian Liu, Xiaowen Liu, Tiger Wang, Canan Huang, Tong Xiao, Jingbo Zhu


Abstract
This paper describes the submission of the NiuTrans end-to-end speech translation system for the IWSLT 2021 offline task, which translates from the English audio to German text directly without intermediate transcription. We use the Transformer-based model architecture and enhance it by Conformer, relative position encoding, and stacked acoustic and textual encoding. To augment the training data, the English transcriptions are translated to German translations. Finally, we employ ensemble decoding to integrate the predictions from several models trained with the different datasets. Combining these techniques, we achieve 33.84 BLEU points on the MuST-C En-De test set, which shows the enormous potential of the end-to-end model.
Anthology ID:
2021.iwslt-1.9
Volume:
Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021)
Month:
August
Year:
2021
Address:
Bangkok, Thailand (online)
Venues:
ACL | IJCNLP | IWSLT
SIG:
SIGSLT
Publisher:
Association for Computational Linguistics
Note:
Pages:
92–99
Language:
URL:
https://aclanthology.org/2021.iwslt-1.9
DOI:
10.18653/v1/2021.iwslt-1.9
Bibkey:
Cite (ACL):
Chen Xu, Xiaoqian Liu, Xiaowen Liu, Tiger Wang, Canan Huang, Tong Xiao, and Jingbo Zhu. 2021. The NiuTrans End-to-End Speech Translation System for IWSLT 2021 Offline Task. In Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021), pages 92–99, Bangkok, Thailand (online). Association for Computational Linguistics.
Cite (Informal):
The NiuTrans End-to-End Speech Translation System for IWSLT 2021 Offline Task (Xu et al., IWSLT 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.iwslt-1.9.pdf
Data
Common VoiceLibriSpeechMuST-COpenSubtitles