Papers by Kurt Micallef
Data Augmentation for Maltese NLP using Transliterated and Machine Translated Arabic Data (2025.findings-emnlp)
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| Challenge: | Maltese is a Semitic language that has evolved under extensive influence from Romance and Germanic languages, particularly Italian and English. |
| Approach: | They investigate whether Arabic-language resources can support Maltese natural language processing . they introduce transliteration schemes and machine translation approaches to align Arabic text with Maltesen . |
| Outcome: | The proposed techniques can significantly improve Maltese natural language processing tasks. |
Cross-Lingual Transfer from Related Languages: Treating Low-Resource Maltese as Multilingual Code-Switching (2024.eacl-long)
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| Challenge: | Multilingual models exhibit impressive cross-lingual transfer capabilities on unseen languages, but performance is impacted when there is a script disparity with the languages used in the model’s pre-training data. |
| Approach: | They propose a novel method to align a resource-rich language's script with a target language and train a classifier that can make informed decisions regarding the appropriate processing of each token. |
| Outcome: | The proposed model can be used to transfer a language's scripts across multiple languages, but it is suboptimal for mixed languages, where only a subset benefits while the rest is impeded. |
Topic Classification and Headline Generation for Maltese Using a Public News Corpus (2024.lrec-main)
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| Challenge: | Existing datasets for low-resource languages lack labelled data . public datasets only cover low-level syntactic tasks . |
| Approach: | They propose to use a news tag multi-label classification and a summary task by generating its title to generate a new semantic dataset for Maltese. |
| Outcome: | The proposed datasets show that current models lack the knowledge required to solve such tasks. |
MELABenchv1: Benchmarking Large Language Models against Smaller Fine-Tuned Models for Low-Resource Maltese NLP (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have shown remarkable performance across various NLP tasks, largely due to their generalisability and ability to perform tasks without additional training. |
| Approach: | They evaluate the performance of 55 publicly available Large Language Models on Maltese, a low-resource language, using a newly introduced benchmark covering 11 discriminative and generative tasks. |
| Outcome: | The proposed models perform poorly on discriminative and generative tasks and smaller fine-tuned models perform better across all tasks. |