Exploring the Impact of Transliteration on NLP Performance: Treating Maltese as an Arabic Dialect

Kurt Micallef, Fadhl Eryani, Nizar Habash, Houda Bouamor, Claudia Borg

Abstract
Multilingual models such as mBERT have been demonstrated to exhibit impressive crosslingual transfer for a number of languages. Despite this, the performance drops for lowerresourced languages, especially when they are not part of the pre-training setup and when there are script differences. In this work we consider Maltese, a low-resource language of Arabic and Romance origins written in Latin script. Specifically, we investigate the impact of transliterating Maltese into Arabic scipt on a number of downstream tasks: Part-of-Speech Tagging, Dependency Parsing, and Sentiment Analysis. We compare multiple transliteration pipelines ranging from deterministic character maps to more sophisticated alternatives, including manually annotated word mappings and non-deterministic character mappings. For the latter, we show that selection techniques using n-gram language models of Tunisian Arabic, the dialect with the highest degree of mutual intelligibility to Maltese, yield better results on downstream tasks. Moreover, our experiments highlight that the use of an Arabic pre-trained model paired with transliteration outperforms mBERT. Overall, our results show that transliterating Maltese can be considered an option to improve the cross-lingual transfer capabilities.
Anthology ID:
2023.cawl-1.4
Volume:
Proceedings of the Workshop on Computation and Written Language (CAWL 2023)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Kyle Gorman, Richard Sproat, Brian Roark
Venue:
CAWL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
22–32
Language:
URL:
https://aclanthology.org/2023.cawl-1.4
DOI:
10.18653/v1/2023.cawl-1.4
Bibkey:
Cite (ACL):
Kurt Micallef, Fadhl Eryani, Nizar Habash, Houda Bouamor, and Claudia Borg. 2023. Exploring the Impact of Transliteration on NLP Performance: Treating Maltese as an Arabic Dialect. In Proceedings of the Workshop on Computation and Written Language (CAWL 2023), pages 22–32, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Exploring the Impact of Transliteration on NLP Performance: Treating Maltese as an Arabic Dialect (Micallef et al., CAWL 2023)
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https://preview.aclanthology.org/ingest-2024-clasp/2023.cawl-1.4.pdf
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