Abstract
The development of NLP tools for low-resource languages is impeded by the lack of data. While recent unsupervised pre-training approaches ease this requirement, the need for labelled data is crucial to progress the development of such tools. Moreover, publicly available datasets for such languages typically cover low-level syntactic tasks. In this work, we introduce new semantic datasets for Maltese generated automatically using associated metadata from a corpus in the news domain. The datasets are a news tag multi-label classification and a news abstractive summarisation task by generating its title. We also present an evaluation using publicly available models as baselines. Our results show that current models are lacking the semantic knowledge required to solve such tasks, shedding light on the need to use better modelling approaches for Maltese.- Anthology ID:
- 2024.lrec-main.1414
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 16274–16281
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.1414
- DOI:
- Cite (ACL):
- Amit Kumar Chaudhary, Kurt Micallef, and Claudia Borg. 2024. Topic Classification and Headline Generation for Maltese Using a Public News Corpus. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 16274–16281, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Topic Classification and Headline Generation for Maltese Using a Public News Corpus (Chaudhary et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.1414.pdf