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VYDANA, H. KARAFIÁT, M. ŽMOLÍKOVÁ, K. BURGET, L. ČERNOCKÝ, J.
Original Title
Jointly Trained Transformers Models for Spoken Language Translation
Type
conference paper
Language
English
Original Abstract
End-to-End and cascade (ASR-MT) spoken language translation (SLT) systems are reaching comparable performances, however, a large degradation is observed when translating the ASR hypothesis in comparison to using oracle input text. In this work, degradation in performance is reduced by creating an End-to-End differentiable pipeline between the ASR and MT systems. In this work, we train SLT systems with ASR objective as an auxiliary loss and both the networks are connected through the neural hidden representations. This training has an End-to-End differentiable path with respect to the final objective function and utilizes the ASR objective for better optimization. This architecture has improved the BLEU score from 41.21 to 44.69. Ensembling the proposed architecture with independently trained ASR and MT systems further improved the BLEU score from 44.69 to 46.9. All the experiments are reported on English-Portuguese speech translation task using the How2 corpus. The final BLEU score is on-par with the best speech translation system on How2 dataset without using any additional training data and language model and using fewer parameters.
Keywords
Spoken Language Translation, Transformers, Joint training, How2 dataset, Auxiliary loss, ASR objective, Coupled decoding, End-to-End differentiable pipeline.
Authors
VYDANA, H.; KARAFIÁT, M.; ŽMOLÍKOVÁ, K.; BURGET, L.; ČERNOCKÝ, J.
Released
6. 6. 2021
Publisher
IEEE Signal Processing Society
Location
Toronto, Ontario
ISBN
978-1-7281-7605-5
Book
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Pages from
7513
Pages to
7517
Pages count
5
URL
https://www.fit.vut.cz/research/publication/12522/
BibTex
@inproceedings{BUT175791, author="Hari Krishna {Vydana} and Martin {Karafiát} and Kateřina {Žmolíková} and Lukáš {Burget} and Jan {Černocký}", title="Jointly Trained Transformers Models for Spoken Language Translation", booktitle="ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)", year="2021", pages="7513--7517", publisher="IEEE Signal Processing Society", address="Toronto, Ontario", doi="10.1109/ICASSP39728.2021.9414159", isbn="978-1-7281-7605-5", url="https://www.fit.vut.cz/research/publication/12522/" }