%0 Conference Proceedings %T Hierarchical Multi-task learning framework for Isometric-Speech Language Translation %A Bhatnagar, Aakash %A Bhavsar, Nidhir %A Singh, Muskaan %A Motlicek, Petr %Y Salesky, Elizabeth %Y Federico, Marcello %Y Costa-jussà, Marta %S Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022) %D 2022 %8 May %I Association for Computational Linguistics %C Dublin, Ireland (in-person and online) %F bhatnagar-etal-2022-hierarchical %X 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 %R 10.18653/v1/2022.iwslt-1.35 %U https://aclanthology.org/2022.iwslt-1.35 %U https://doi.org/10.18653/v1/2022.iwslt-1.35 %P 379-385