@inproceedings{li-etal-2024-kit,
title = "The {KIT} Speech Translation Systems for {IWSLT} 2024 Dialectal and Low-resource Track",
author = "Li, Zhaolin and
Yavuz Ugan, Enes and
Liu, Danni and
Mullov, Carlos and
Anh Dinh, Tu and
Koneru, Sai and
Waibel, Alexander and
Niehues, Jan",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.27",
pages = "221--228",
abstract = "This paper presents KIT{'}s submissions to the IWSLT 2024 dialectal and low-resource track. In this work, we build systems for translating into English from speech in Maltese, Bemba, and two Arabic dialects Tunisian and North Levantine. Under the unconstrained condition, we leverage the pre-trained multilingual models by fine-tuning them for the target language pairs to address data scarcity problems in this track. We build cascaded and end-to-end speech translation systems for different language pairs and show the cascaded system brings slightly better overall performance. Besides, we find utilizing additional data resources boosts speech recognition performance but slightly harms machine translation performance in cascaded systems. Lastly, we show that Minimum Bayes Risk is effective in improving speech translation performance by combining the cascaded and end-to-end systems, bringing a consistent improvement of around 1 BLUE point.",
}
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<abstract>This paper presents KIT’s submissions to the IWSLT 2024 dialectal and low-resource track. In this work, we build systems for translating into English from speech in Maltese, Bemba, and two Arabic dialects Tunisian and North Levantine. Under the unconstrained condition, we leverage the pre-trained multilingual models by fine-tuning them for the target language pairs to address data scarcity problems in this track. We build cascaded and end-to-end speech translation systems for different language pairs and show the cascaded system brings slightly better overall performance. Besides, we find utilizing additional data resources boosts speech recognition performance but slightly harms machine translation performance in cascaded systems. Lastly, we show that Minimum Bayes Risk is effective in improving speech translation performance by combining the cascaded and end-to-end systems, bringing a consistent improvement of around 1 BLUE point.</abstract>
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%0 Conference Proceedings
%T The KIT Speech Translation Systems for IWSLT 2024 Dialectal and Low-resource Track
%A Li, Zhaolin
%A Yavuz Ugan, Enes
%A Liu, Danni
%A Mullov, Carlos
%A Anh Dinh, Tu
%A Koneru, Sai
%A Waibel, Alexander
%A Niehues, Jan
%Y Salesky, Elizabeth
%Y Federico, Marcello
%Y Carpuat, Marine
%S Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand (in-person and online)
%F li-etal-2024-kit
%X This paper presents KIT’s submissions to the IWSLT 2024 dialectal and low-resource track. In this work, we build systems for translating into English from speech in Maltese, Bemba, and two Arabic dialects Tunisian and North Levantine. Under the unconstrained condition, we leverage the pre-trained multilingual models by fine-tuning them for the target language pairs to address data scarcity problems in this track. We build cascaded and end-to-end speech translation systems for different language pairs and show the cascaded system brings slightly better overall performance. Besides, we find utilizing additional data resources boosts speech recognition performance but slightly harms machine translation performance in cascaded systems. Lastly, we show that Minimum Bayes Risk is effective in improving speech translation performance by combining the cascaded and end-to-end systems, bringing a consistent improvement of around 1 BLUE point.
%U https://aclanthology.org/2024.iwslt-1.27
%P 221-228
Markdown (Informal)
[The KIT Speech Translation Systems for IWSLT 2024 Dialectal and Low-resource Track](https://aclanthology.org/2024.iwslt-1.27) (Li et al., IWSLT 2024)
ACL
- Zhaolin Li, Enes Yavuz Ugan, Danni Liu, Carlos Mullov, Tu Anh Dinh, Sai Koneru, Alexander Waibel, and Jan Niehues. 2024. The KIT Speech Translation Systems for IWSLT 2024 Dialectal and Low-resource Track. In Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024), pages 221–228, Bangkok, Thailand (in-person and online). Association for Computational Linguistics.