Evaluating the Potential of Language-family-specific Generative Models for Low-resource Data Augmentation: A Faroese Case Study

Barbara Scalvini, Iben Nyholm Debess

Abstract
We investigate GPT-SW3, a generative language model for the Nordic languages, to assess its understanding of the low-resourced Faroese language. Our aim is to demonstrate the advantages of using language-family-specific generative models to augment data for related languages with fewer resources. We evaluate GPT-SW3 by prompting it for Faroese to English translation in a zero, one, and few-shot setting. We assess such translations with an ensemble score consisting of an arithmetic average between the BLEU and a semantic similarity score (SBERT). Moreover, we challenge the model’s Faroese language understanding capabilities on a small dataset of curated Faroese trick sentences. There, we make a qualitative comparison of the model’s performance with respect to Open AI’s GPT-3.5 and GPT-4, demonstrating the advantages of using a language-family-specific generative model for navigating non-trivial scenarios. We evaluate the pipeline thus created and use it, as a proof of concept, to create an automatically annotated Faroese semantic textual similarity (STS) dataset.
Anthology ID:
2024.lrec-main.576
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
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Publisher:
ELRA and ICCL
Note:
Pages:
6496–6503
Language:
URL:
https://aclanthology.org/2024.lrec-main.576
DOI:
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Cite (ACL):
Barbara Scalvini and Iben Nyholm Debess. 2024. Evaluating the Potential of Language-family-specific Generative Models for Low-resource Data Augmentation: A Faroese Case Study. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 6496–6503, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Evaluating the Potential of Language-family-specific Generative Models for Low-resource Data Augmentation: A Faroese Case Study (Scalvini & Debess, LREC-COLING 2024)
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