UM IWSLT 2024 Low-Resource Speech Translation: Combining Maltese and North Levantine Arabic

Sara Nabhani, Aiden Williams, Miftahul Jannat, Kate Rebecca Belcher, Melanie Galea, Anna Taylor, Kurt Micallef, Claudia Borg

Abstract
The IWSLT low-resource track encourages innovation in the field of speech translation, particularly in data-scarce conditions. This paper details our submission for the IWSLT 2024 low-resource track shared task for Maltese-English and North Levantine Arabic-English spoken language translation using an unconstrained pipeline approach. Using language models, we improve ASR performance by correcting the produced output. We present a 2 step approach for MT using data from external sources showing improvements over baseline systems. We also explore transliteration as a means to further augment MT data and exploit the cross-lingual similarities between Maltese and Arabic.
Anthology ID:
2024.iwslt-1.14
Volume:
Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)
Month:
August
Year:
2024
Address:
Bangkok, Thailand (in-person and online)
Editors:
Elizabeth Salesky, Marcello Federico, Marine Carpuat
Venue:
IWSLT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
97–107
Language:
URL:
https://aclanthology.org/2024.iwslt-1.14
DOI:
10.18653/v1/2024.iwslt-1.14
Bibkey:
Cite (ACL):
Sara Nabhani, Aiden Williams, Miftahul Jannat, Kate Rebecca Belcher, Melanie Galea, Anna Taylor, Kurt Micallef, and Claudia Borg. 2024. UM IWSLT 2024 Low-Resource Speech Translation: Combining Maltese and North Levantine Arabic. In Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024), pages 97–107, Bangkok, Thailand (in-person and online). Association for Computational Linguistics.
Cite (Informal):
UM IWSLT 2024 Low-Resource Speech Translation: Combining Maltese and North Levantine Arabic (Nabhani et al., IWSLT 2024)
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