Hierarchical Multi-task learning framework for Isometric-Speech Language Translation

Aakash Bhatnagar, Nidhir Bhavsar, Muskaan Singh, Petr Motlicek


Abstract
This paper presents our submission for the shared task on isometric neural machine translation in International Conference on Spoken Language Translation (IWSLT). There are numerous state-of-art models for translation problems. However, these models lack any length constraint to produce short or long outputs from the source text. In this paper, we propose a hierarchical approach to generate isometric translation on MUST-C dataset, we achieve a BERTscore of 0.85, a length ratio of 1.087, a BLEU score of 42.3, and a length range of 51.03%. On the blind dataset provided by the task organizers, we obtain a BERTscore of 0.80, a length ratio of 1.10 and a length range of 47.5%. We have made our code public here https://github.com/aakash0017/Machine-Translation-ISWLT
Anthology ID:
2022.iwslt-1.35
Volume:
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)
Month:
May
Year:
2022
Address:
Dublin, Ireland (in-person and online)
Editors:
Elizabeth Salesky, Marcello Federico, Marta Costa-jussà
Venue:
IWSLT
SIG:
SIGSLT
Publisher:
Association for Computational Linguistics
Note:
Pages:
379–385
Language:
URL:
https://aclanthology.org/2022.iwslt-1.35
DOI:
10.18653/v1/2022.iwslt-1.35
Bibkey:
Cite (ACL):
Aakash Bhatnagar, Nidhir Bhavsar, Muskaan Singh, and Petr Motlicek. 2022. Hierarchical Multi-task learning framework for Isometric-Speech Language Translation. In Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022), pages 379–385, Dublin, Ireland (in-person and online). Association for Computational Linguistics.
Cite (Informal):
Hierarchical Multi-task learning framework for Isometric-Speech Language Translation (Bhatnagar et al., IWSLT 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.iwslt-1.35.pdf
Code
 aakash0017/machine-translation-iswlt
Data
MuST-COPUS-MTPAWS-X