Cross-Lingual Transfer from Related Languages: Treating Low-Resource Maltese as Multilingual Code-Switching
Kurt Micallef, Nizar Habash, Claudia Borg, Fadhl Eryani, Houda Bouamor
Abstract
Although multilingual language models exhibit impressive cross-lingual transfer capabilities on unseen languages, the performance on downstream tasks is impacted when there is a script disparity with the languages used in the multilingual model’s pre-training data. Using transliteration offers a straightforward yet effective means to align the script of a resource-rich language with a target language thereby enhancing cross-lingual transfer capabilities. However, for mixed languages, this approach is suboptimal, since only a subset of the language benefits from the cross-lingual transfer while the remainder is impeded. In this work, we focus on Maltese, a Semitic language, with substantial influences from Arabic, Italian, and English, and notably written in Latin script. We present a novel dataset annotated with word-level etymology. We use this dataset to train a classifier that enables us to make informed decisions regarding the appropriate processing of each token in the Maltese language. We contrast indiscriminate transliteration or translation to mixing processing pipelines that only transliterate words of Arabic origin, thereby resulting in text with a mixture of scripts. We fine-tune the processed data on four downstream tasks and show that conditional transliteration based on word etymology yields the best results, surpassing fine-tuning with raw Maltese or Maltese processed with non-selective pipelines.- Anthology ID:
- 2024.eacl-long.61
- Volume:
- Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- March
- Year:
- 2024
- Address:
- St. Julian’s, Malta
- Editors:
- Yvette Graham, Matthew Purver
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1014–1025
- Language:
- URL:
- https://aclanthology.org/2024.eacl-long.61
- DOI:
- Cite (ACL):
- Kurt Micallef, Nizar Habash, Claudia Borg, Fadhl Eryani, and Houda Bouamor. 2024. Cross-Lingual Transfer from Related Languages: Treating Low-Resource Maltese as Multilingual Code-Switching. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1014–1025, St. Julian’s, Malta. Association for Computational Linguistics.
- Cite (Informal):
- Cross-Lingual Transfer from Related Languages: Treating Low-Resource Maltese as Multilingual Code-Switching (Micallef et al., EACL 2024)
- PDF:
- https://preview.aclanthology.org/landing_page/2024.eacl-long.61.pdf