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        <title>I2R’s End-to-End Speech Translation System for IWSLT 2023 Offline Shared Task</title>
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        <namePart type="given">Muhammad</namePart>
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    <name type="personal">
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            <title>Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)</title>
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            <namePart type="family">Salesky</namePart>
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    <abstract>This paper describes I2R’s submission to the offline speech translation track for IWSLT 2023. We focus on an end-to-end approach for translation from English audio to German text, one of the three available language directions in this year’s edition. The I2R system leverages on pretrained models that have been exposed to large-scale audio and text data for our base model. We introduce several stages of additional pretraining followed by fine-tuning to adapt the system for the downstream speech translation task. The strategy is supplemented by other techniques such as data augmentation, domain tagging, knowledge distillation, and model ensemble, among others. We evaluate the system on several publicly available test sets for comparison.</abstract>
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%0 Conference Proceedings
%T I2R’s End-to-End Speech Translation System for IWSLT 2023 Offline Shared Task
%A Huzaifah, Muhammad
%A Min Tan, Kye
%A Duan, Richeng
%Y Salesky, Elizabeth
%Y Federico, Marcello
%Y Carpuat, Marine
%S Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada (in-person and online)
%F huzaifah-etal-2023-i2rs
%X This paper describes I2R’s submission to the offline speech translation track for IWSLT 2023. We focus on an end-to-end approach for translation from English audio to German text, one of the three available language directions in this year’s edition. The I2R system leverages on pretrained models that have been exposed to large-scale audio and text data for our base model. We introduce several stages of additional pretraining followed by fine-tuning to adapt the system for the downstream speech translation task. The strategy is supplemented by other techniques such as data augmentation, domain tagging, knowledge distillation, and model ensemble, among others. We evaluate the system on several publicly available test sets for comparison.
%R 10.18653/v1/2023.iwslt-1.16
%U https://aclanthology.org/2023.iwslt-1.16
%U https://doi.org/10.18653/v1/2023.iwslt-1.16
%P 202-210

Markdown (Informal)

[I2R’s End-to-End Speech Translation System for IWSLT 2023 Offline Shared Task](https://aclanthology.org/2023.iwslt-1.16) (Huzaifah et al., IWSLT 2023)

• I2R’s End-to-End Speech Translation System for IWSLT 2023 Offline Shared Task (Huzaifah et al., IWSLT 2023)

ACL

• Muhammad Huzaifah, Kye Min Tan, and Richeng Duan. 2023. I2R’s End-to-End Speech Translation System for IWSLT 2023 Offline Shared Task. In Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023), pages 202–210, Toronto, Canada (in-person and online). Association for Computational Linguistics.