@inproceedings{nabhani-etal-2024-um,
title = "{UM} {IWSLT} 2024 Low-Resource Speech Translation: Combining {M}altese and {N}orth {L}evantine {A}rabic",
author = "Nabhani, Sara and
Williams, Aiden and
Jannat, Miftahul and
Rebecca Belcher, Kate and
Galea, Melanie and
Taylor, Anna and
Micallef, Kurt and
Borg, Claudia",
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-1.14/",
doi = "10.18653/v1/2024.iwslt-1.14",
pages = "97--107",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T UM IWSLT 2024 Low-Resource Speech Translation: Combining Maltese and North Levantine Arabic
%A Nabhani, Sara
%A Williams, Aiden
%A Jannat, Miftahul
%A Rebecca Belcher, Kate
%A Galea, Melanie
%A Taylor, Anna
%A Micallef, Kurt
%A Borg, Claudia
%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 nabhani-etal-2024-um
%X 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.
%R 10.18653/v1/2024.iwslt-1.14
%U https://aclanthology.org/2024.iwslt-1.14/
%U https://doi.org/10.18653/v1/2024.iwslt-1.14
%P 97-107
Markdown (Informal)
[UM IWSLT 2024 Low-Resource Speech Translation: Combining Maltese and North Levantine Arabic](https://aclanthology.org/2024.iwslt-1.14/) (Nabhani et al., IWSLT 2024)
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.