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.

Sentiment analysis in low-resource languages presents unique challenges that Large Language Models may help address. This study explores the efficacy of GPT-4 for sentiment analysis on Faroese news texts, an uncharted task for this language. On the basis of guidelines presented, the sentiment analysis was performed with a multi-class approach at the sentence and document level with 225 sentences analysed in 170 articles. When comparing GPT-4 to human annotators, we observe that GPT-4 performs remarkably well. We explored two prompt configurations and observed a benefit from having clear instructions for the sentiment analysis task, but no benefit from translating the articles to English before the sentiment analysis task. Our results indicate that GPT-4 can be considered as a valuable tool for generating Faroese test data. Furthermore, our investigation reveals the intricacy of news sentiment. This motivates a more nuanced approach going forward, and we suggest a multi-label approach for future research in this domain. We further explored the efficacy of GPT-4 in topic classification on news texts and observed more negative sentiments expressed in international than national news. Overall, this work demonstrates GPT-4’s proficiency on a novel task and its utility for augmenting resources in low-data languages.

The biggest challenges we face in developing LR and LT for Faroese is the lack of existing resources. A few resources already exist for Faroese, but many of them are either of insufficient size and quality or are not easily accessible. Therefore, the Faroese ASR project, Ravnur, set out to make a BLARK for Faroese. The BLARK is still in the making, but many of its resources have already been produced or collected. The LR status is framed by mentioning existing LR of relevant size and quality. The specific components of the BLARK are presented as well as the working principles behind the BLARK. The BLARK will be a pillar in Faroese LR, being relatively substantial in both size, quality, and diversity. It will be open-source, inviting other small languages to use it as an inspiration to create their own BLARK. We comment on the faulty yet sprouting LT situation in the Faroe Islands. The LR and LT challenges are not solved with just a BLARK. Some initiatives are therefore proposed to better the prospects of Faroese LT. The open-source principle of the project should facilitate further development.

We present a new method for preparing a lexical-phonetic database as a resource for acoustic model training. The research is an offshoot of the ongoing Project Ravnur (Speech Recognition for Faroese), but the method is language-independent. At NODALIDA 2019 we demonstrate the method (called SHARP) online, showing how a traditional lexical-phonetic dictionary (with a very rich phone inventory) is transformed into an ASR-friendly database (with reduced phonetics, preventing data sparseness). The mapping procedure is informed by a corpus of speech transcripts. We conclude with a discussion on the benefits of a well-thought-out BLARK design (Basic Language Resource Kit), making tools like SHARP possible.