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            <title>Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021)</title>
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            <namePart type="given">Alex</namePart>
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            <namePart type="given">Marta</namePart>
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    <abstract>This paper describes the submission to the IWSLT 2021 offline speech translation task by the UPC Machine Translation group. The task consists of building a system capable of translating English audio recordings extracted from TED talks into German text. Submitted systems can be either cascade or end-to-end and use a custom or given segmentation. Our submission is an end-to-end speech translation system, which combines pre-trained models (Wav2Vec 2.0 and mBART) with coupling modules between the encoder and decoder, and uses an efficient fine-tuning technique, which trains only 20% of its total parameters. We show that adding an Adapter to the system and pre-training it, can increase the convergence speed and the final result, with which we achieve a BLEU score of 27.3 on the MuST-C test set. Our final model is an ensemble that obtains 28.22 BLEU score on the same set. Our submission also uses a custom segmentation algorithm that employs pre-trained Wav2Vec 2.0 for identifying periods of untranscribable text and can bring improvements of 2.5 to 3 BLEU score on the IWSLT 2019 test set, as compared to the result with the given segmentation.</abstract>
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    <identifier type="doi">10.18653/v1/2021.iwslt-1.11</identifier>
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%0 Conference Proceedings
%T End-to-End Speech Translation with Pre-trained Models and Adapters: UPC at IWSLT 2021
%A Gállego, Gerard I.
%A Tsiamas, Ioannis
%A Escolano, Carlos
%A Fonollosa, José A. R.
%A Costa-jussà, Marta R.
%Y Federico, Marcello
%Y Waibel, Alex
%Y Costa-jussà, Marta R.
%Y Niehues, Jan
%Y Stuker, Sebastian
%Y Salesky, Elizabeth
%S Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand (online)
%F gallego-etal-2021-end
%X This paper describes the submission to the IWSLT 2021 offline speech translation task by the UPC Machine Translation group. The task consists of building a system capable of translating English audio recordings extracted from TED talks into German text. Submitted systems can be either cascade or end-to-end and use a custom or given segmentation. Our submission is an end-to-end speech translation system, which combines pre-trained models (Wav2Vec 2.0 and mBART) with coupling modules between the encoder and decoder, and uses an efficient fine-tuning technique, which trains only 20% of its total parameters. We show that adding an Adapter to the system and pre-training it, can increase the convergence speed and the final result, with which we achieve a BLEU score of 27.3 on the MuST-C test set. Our final model is an ensemble that obtains 28.22 BLEU score on the same set. Our submission also uses a custom segmentation algorithm that employs pre-trained Wav2Vec 2.0 for identifying periods of untranscribable text and can bring improvements of 2.5 to 3 BLEU score on the IWSLT 2019 test set, as compared to the result with the given segmentation.
%R 10.18653/v1/2021.iwslt-1.11
%U https://aclanthology.org/2021.iwslt-1.11
%U https://doi.org/10.18653/v1/2021.iwslt-1.11
%P 110-119

Markdown (Informal)

[End-to-End Speech Translation with Pre-trained Models and Adapters: UPC at IWSLT 2021](https://aclanthology.org/2021.iwslt-1.11) (Gállego et al., IWSLT 2021)

• End-to-End Speech Translation with Pre-trained Models and Adapters: UPC at IWSLT 2021 (Gállego et al., IWSLT 2021)

ACL

• Gerard I. Gállego, Ioannis Tsiamas, Carlos Escolano, José A. R. Fonollosa, and Marta R. Costa-jussà. 2021. End-to-End Speech Translation with Pre-trained Models and Adapters: UPC at IWSLT 2021. In Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021), pages 110–119, Bangkok, Thailand (online). Association for Computational Linguistics.