@inproceedings{rishu-etal-2024-uom,
title = "{U}o{M}-{DFKI} submission to the low resource shared task",
author = "Rishu, Kumar and
Williams, Aiden and
Borg, Claudia and
Ostermann, Simon",
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-1.33",
pages = "280--285",
abstract = "This system description paper presents the details of our primary and contrastive approaches to translating Maltese into English for IWSLT 24. The Maltese language shares a large vocabulary with Arabic and Italian languages, thus making it an ideal candidate to test the cross-lingual capabilities of recent state-of-the-art models. We experiment with two end-to-end approaches for our submissions: the Whisper and wav2vec 2.0 models. Our primary system gets a BLEU score of 35.1 on the combined data, whereas our contrastive approach gets 18.5. We also provide a manual analysis of our contrastive approach to identify some pitfalls that may have caused this difference.",
}
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<abstract>This system description paper presents the details of our primary and contrastive approaches to translating Maltese into English for IWSLT 24. The Maltese language shares a large vocabulary with Arabic and Italian languages, thus making it an ideal candidate to test the cross-lingual capabilities of recent state-of-the-art models. We experiment with two end-to-end approaches for our submissions: the Whisper and wav2vec 2.0 models. Our primary system gets a BLEU score of 35.1 on the combined data, whereas our contrastive approach gets 18.5. We also provide a manual analysis of our contrastive approach to identify some pitfalls that may have caused this difference.</abstract>
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%0 Conference Proceedings
%T UoM-DFKI submission to the low resource shared task
%A Rishu, Kumar
%A Williams, Aiden
%A Borg, Claudia
%A Ostermann, Simon
%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 rishu-etal-2024-uom
%X This system description paper presents the details of our primary and contrastive approaches to translating Maltese into English for IWSLT 24. The Maltese language shares a large vocabulary with Arabic and Italian languages, thus making it an ideal candidate to test the cross-lingual capabilities of recent state-of-the-art models. We experiment with two end-to-end approaches for our submissions: the Whisper and wav2vec 2.0 models. Our primary system gets a BLEU score of 35.1 on the combined data, whereas our contrastive approach gets 18.5. We also provide a manual analysis of our contrastive approach to identify some pitfalls that may have caused this difference.
%U https://aclanthology.org/2024.iwslt-1.33
%P 280-285
Markdown (Informal)
[UoM-DFKI submission to the low resource shared task](https://aclanthology.org/2024.iwslt-1.33) (Rishu et al., IWSLT 2024)
ACL
- Kumar Rishu, Aiden Williams, Claudia Borg, and Simon Ostermann. 2024. UoM-DFKI submission to the low resource shared task. In Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024), pages 280–285, Bangkok, Thailand (in-person and online). Association for Computational Linguistics.