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        <title>Hierarchical Multi-task learning framework for Isometric-Speech Language Translation</title>
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        <namePart type="given">Aakash</namePart>
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        <namePart type="given">Nidhir</namePart>
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        <namePart type="given">Muskaan</namePart>
        <namePart type="family">Singh</namePart>
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    <name type="personal">
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            <title>Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)</title>
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            <namePart type="given">Marta</namePart>
            <namePart type="family">Costa-jussà</namePart>
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    <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</abstract>
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    <identifier type="doi">10.18653/v1/2022.iwslt-1.35</identifier>
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%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

Markdown (Informal)

[Hierarchical Multi-task learning framework for Isometric-Speech Language Translation](https://aclanthology.org/2022.iwslt-1.35) (Bhatnagar et al., IWSLT 2022)

• Hierarchical Multi-task learning framework for Isometric-Speech Language Translation (Bhatnagar et al., IWSLT 2022)

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.