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        <title>Data Augmentation for Maltese NLP using Transliterated and Machine Translated Arabic Data</title>
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        <namePart type="given">Kurt</namePart>
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    <name type="personal">
        <namePart type="given">Nizar</namePart>
        <namePart type="family">Habash</namePart>
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    <name type="personal">
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            <namePart type="given">Christos</namePart>
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            <namePart type="given">Tanmoy</namePart>
            <namePart type="family">Chakraborty</namePart>
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            <namePart type="given">Carolyn</namePart>
            <namePart type="family">Rose</namePart>
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    <abstract>Maltese is a unique Semitic language that has evolved under extensive influence from Romance and Germanic languages, particularly Italian and English. Despite its Semitic roots, its orthography is based on the Latin script, creating a gap between it and its closest linguistic relatives in Arabic. In this paper, we explore whether Arabic-language resources can support Maltese natural language processing (NLP) through cross-lingual augmentation techniques. We investigate multiple strategies for aligning Arabic textual data with Maltese, including various transliteration schemes and machine translation (MT) approaches. As part of this, we also introduce novel transliteration systems that better represent Maltese orthography. We evaluate the impact of these augmentations on monolingual and mutlilingual models and demonstrate that Arabic-based augmentation can significantly benefit Maltese NLP tasks.</abstract>
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%0 Conference Proceedings
%T Data Augmentation for Maltese NLP using Transliterated and Machine Translated Arabic Data
%A Micallef, Kurt
%A Habash, Nizar
%A Borg, Claudia
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F micallef-etal-2025-data
%X Maltese is a unique Semitic language that has evolved under extensive influence from Romance and Germanic languages, particularly Italian and English. Despite its Semitic roots, its orthography is based on the Latin script, creating a gap between it and its closest linguistic relatives in Arabic. In this paper, we explore whether Arabic-language resources can support Maltese natural language processing (NLP) through cross-lingual augmentation techniques. We investigate multiple strategies for aligning Arabic textual data with Maltese, including various transliteration schemes and machine translation (MT) approaches. As part of this, we also introduce novel transliteration systems that better represent Maltese orthography. We evaluate the impact of these augmentations on monolingual and mutlilingual models and demonstrate that Arabic-based augmentation can significantly benefit Maltese NLP tasks.
%U https://aclanthology.org/2025.findings-emnlp.1177/
%P 21580-21590

Markdown (Informal)

[Data Augmentation for Maltese NLP using Transliterated and Machine Translated Arabic Data](https://aclanthology.org/2025.findings-emnlp.1177/) (Micallef et al., Findings 2025)

• Data Augmentation for Maltese NLP using Transliterated and Machine Translated Arabic Data (Micallef et al., Findings 2025)

ACL

• Kurt Micallef, Nizar Habash, and Claudia Borg. 2025. Data Augmentation for Maltese NLP using Transliterated and Machine Translated Arabic Data. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 21580–21590, Suzhou, China. Association for Computational Linguistics.