End-to-end Speech Translation System Description of LIT for IWSLT 2019

Mei Tu, Wei Liu, Lijie Wang, Xiao Chen, Xue Wen

Abstract
This paper describes our end-to-end speech translation system for the speech translation task of lectures and TED talks from English to German for IWSLT Evaluation 2019. We propose layer-tied self-attention for end-to-end speech translation. Our method takes advantage of sharing weights of speech encoder and text decoder. The representation of source speech and the representation of target text are coordinated layer by layer, so that the speech and text can learn a better alignment during the training procedure. We also adopt data augmentation to enhance the parallel speech-text corpus. The En-De experimental results show that our best model achieves 17.68 on tst2015. Our ASR achieves WER of 6.6% on TED-LIUM test set. The En-Pt model can achieve about 11.83 on the MuST-C dev set.
Anthology ID:
2019.iwslt-1.7
Volume:
Proceedings of the 16th International Conference on Spoken Language Translation
Month:
November 2-3
Year:
2019
Address:
Hong Kong
Editors:
Jan Niehues, Rolando Cattoni, Sebastian Stüker, Matteo Negri, Marco Turchi, Thanh-Le Ha, Elizabeth Salesky, Ramon Sanabria, Loic Barrault, Lucia Specia, Marcello Federico
Venue:
IWSLT
SIG:
SIGSLT
Publisher:
Association for Computational Linguistics
Note:
Pages:
Language:
URL:
https://aclanthology.org/2019.iwslt-1.7
DOI:
Bibkey:
Cite (ACL):
Mei Tu, Wei Liu, Lijie Wang, Xiao Chen, and Xue Wen. 2019. End-to-end Speech Translation System Description of LIT for IWSLT 2019. In Proceedings of the 16th International Conference on Spoken Language Translation, Hong Kong. Association for Computational Linguistics.
Cite (Informal):
End-to-end Speech Translation System Description of LIT for IWSLT 2019 (Tu et al., IWSLT 2019)
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PDF:
https://preview.aclanthology.org/dois-2013-emnlp/2019.iwslt-1.7.pdf
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