The USYD-JD Speech Translation System for IWSLT2021

Liang Ding, Dacheng Tao


Abstract
This paper describes the University of Sydney & JD’s joint submission of the IWSLT 2021 low resource speech translation task. We participated in the Swahili->English direction and got the best scareBLEU (25.3) score among all the participants. Our constrained system is based on a pipeline framework, i.e. ASR and NMT. We trained our models with the officially provided ASR and MT datasets. The ASR system is based on the open-sourced tool Kaldi and this work mainly explores how to make the most of the NMT models. To reduce the punctuation errors generated by the ASR model, we employ our previous work SlotRefine to train a punctuation correction model. To achieve better translation performance, we explored the most recent effective strategies, including back translation, knowledge distillation, multi-feature reranking, and transductive finetuning. For model structure, we tried auto-regressive and non-autoregressive models, respectively. In addition, we proposed two novel pre-train approaches, i.e. de-noising training and bidirectional training to fully exploit the data. Extensive experiments show that adding the above techniques consistently improves the BLEU scores, and the final submission system outperforms the baseline (Transformer ensemble model trained with the original parallel data) by approximately 10.8 BLEU score, achieving the SOTA performance.
Anthology ID:
2021.iwslt-1.22
Volume:
Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021)
Month:
August
Year:
2021
Address:
Bangkok, Thailand (online)
Venue:
IWSLT
SIG:
SIGSLT
Publisher:
Association for Computational Linguistics
Note:
Pages:
182–191
Language:
URL:
https://aclanthology.org/2021.iwslt-1.22
DOI:
10.18653/v1/2021.iwslt-1.22
Bibkey:
Cite (ACL):
Liang Ding and Dacheng Tao. 2021. The USYD-JD Speech Translation System for IWSLT2021. In Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021), pages 182–191, Bangkok, Thailand (online). Association for Computational Linguistics.
Cite (Informal):
The USYD-JD Speech Translation System for IWSLT2021 (Ding & Tao, IWSLT 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/author-url/2021.iwslt-1.22.pdf
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