Effective combination of pretrained models - KIT@IWSLT2022

Ngoc-Quan Pham, Tuan Nam Nguyen, Thai-Binh Nguyen, Danni Liu, Carlos Mullov, Jan Niehues, Alexander Waibel


Abstract
Pretrained models in acoustic and textual modalities can potentially improve speech translation for both Cascade and End-to-end approaches. In this evaluation, we aim at empirically looking for the answer by using the wav2vec, mBART50 and DeltaLM models to improve text and speech translation models. The experiments showed that the presence of these models together with an advanced audio segmentation method results in an improvement over the previous end-to-end system by up to 7 BLEU points. More importantly, the experiments showed that given enough data and modeling capacity to overcome the training difficulty, we can outperform even very competitive Cascade systems. In our experiments, this gap can be as large as 2.0 BLEU points, the same gap that the Cascade often led over the years.
Anthology ID:
2022.iwslt-1.14
Volume:
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)
Month:
May
Year:
2022
Address:
Dublin, Ireland (in-person and online)
Venues:
ACL | IWSLT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
190–197
Language:
URL:
https://aclanthology.org/2022.iwslt-1.14
DOI:
10.18653/v1/2022.iwslt-1.14
Bibkey:
Cite (ACL):
Ngoc-Quan Pham, Tuan Nam Nguyen, Thai-Binh Nguyen, Danni Liu, Carlos Mullov, Jan Niehues, and Alexander Waibel. 2022. Effective combination of pretrained models - KIT@IWSLT2022. In Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022), pages 190–197, Dublin, Ireland (in-person and online). Association for Computational Linguistics.
Cite (Informal):
Effective combination of pretrained models - KIT@IWSLT2022 (Pham et al., IWSLT 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.iwslt-1.14.pdf
Data
How2LibriSpeechMuST-C