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.- Anthology ID:
- 2024.iwslt-1.33
- 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:
- 280–285
- Language:
- URL:
- https://aclanthology.org/2024.iwslt-1.33
- DOI:
- Cite (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.
- Cite (Informal):
- UoM-DFKI submission to the low resource shared task (Rishu et al., IWSLT 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.iwslt-1.33.pdf