Papers with Maltese
Multilingual Verbalisation of Knowledge Graphs (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Most work on Knowledge Graph (KG) verbalisation is monolingual leaving open the question of how to scale KG-to-Text generation to languages with varying amounts of resources. |
| Approach: | They explore how to scale KG-to-Text generation to languages with varying resources . they construct multilingual training data and test data for each language . |
| Outcome: | The proposed approach performs best on all 9 languages, compared with other approaches on low vs high resource languages and on in- v. out-of-domain data. |
Cross-Lingual Transfer from Related Languages: Treating Low-Resource Maltese as Multilingual Code-Switching (2024.eacl-long)
Copied to clipboard
| 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. |
High-quality Data-to-Text Generation for Severely Under-Resourced Languages with Out-of-the-box Large Language Models (2024.findings-eacl)
Copied to clipboard
| Challenge: | Pretrained large language models (LLMs) can bridge the performance gap for under-resourced languages by substantial margins, as measured by both automatic and human evaluations. |
| Approach: | They propose to use pretrained large language models to bridge this gap by automating and evaluating data-to-text generation in under-resourced languages. |
| Outcome: | The proposed model can set the state of the art for under-resourced languages by substantial margins, as measured by both automatic and human evaluations. |
Dirichlet-Smoothed Word Embeddings for Low-Resource Settings (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing count-based word embeddings are superseded by machine-learning methods like word2vec and GloVe, but in many settings there is not much text data available. |
| Approach: | They propose to use positive pointwise mutual information (PPMI) weighted co-occurrence matrices to compute word embeddings from a corpus using large amounts of text data. |
| Outcome: | The proposed method outperforms word2vec and the state-of-the-art for low-resource settings and obtains competitive results for Maltese and Luxembourgish. |
Semantic Evaluation of Multilingual Data-to-Text Generation via NLI Fine-Tuning: Precision, Recall and F1 scores (2025.findings-acl)
Copied to clipboard
| Challenge: | KG-to-Text models are prone to errors like Additions and Omissions, and few languages are taken into account since both train and test data are not readily available. |
| Approach: | They propose a multilingual evaluation framework that is reference-less . it allows estimating how much a KG-to-Text Model under- (omission) or over- (addition) generates. |
| Outcome: | The proposed evaluation framework outperforms prior reference-less metrics in correlation with human judgments and provides scores for precision and recall. |
MASRI-HEADSET: A Maltese Corpus for Speech Recognition (2020.lrec-1)
Copied to clipboard
Carlos Daniel Hernandez Mena, Albert Gatt, Andrea DeMarco, Claudia Borg, Lonneke van der Plas, Amanda Muscat, Ian Padovani
| Challenge: | Maltese is the national language of Malta and is spoken by approximately 500,000 people. |
| Approach: | They present the first spoken Maltese corpus designed purposely for Automatic Speech Recognition (ASR) it consists of 8 hours of speech paired with text, recorded by using short text snippets in a laboratory environment. |
| Outcome: | The MASRI-HEADSET corpus was developed by the MASR project at the University of Malta. |
MELABenchv1: Benchmarking Large Language Models against Smaller Fine-Tuned Models for Low-Resource Maltese NLP (2025.findings-acl)
Copied to clipboard
| 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. |
Data Augmentation for Maltese NLP using Transliterated and Machine Translated Arabic Data (2025.findings-emnlp)
Copied to clipboard
| 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. |
Topic Classification and Headline Generation for Maltese Using a Public News Corpus (2024.lrec-main)
Copied to clipboard
| 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. |