Year: 2026
Type: conference-paper
Abstract: The performance of large language models (LLMs) tends to degrade for extremely lowresource languages, primarily due to the lack of labeled training data.Despite growing interest, the availability of high-quality natural language processing (NLP) datasets for these languages remains limited.This paper addresses such gap by focusing on Ladin, an endangered Romance language, specifically the Val Badia variant.Leveraging a small set of parallel Ladin-Italian sentence pairs, we create synthetic datasets for sentiment analysis and question answering by translating monolingual Italian data.To ensure linguistic quality, we apply rigorous filtering and back-translation procedures in our method.We further demonstrate that incorporating these synthetic datasets into machine translation training leads to substantial improvements over existing Italian-Ladin translation baselines.Our contributions include sentiment analysis and question answering datasets for Ladin, establishing foundational resources that support broader NLP research and downstream applications for underrepresented languages.
Authors: Ulin Nuha, Adam Jatowt
Institutions: Toyota Technological Institute, Universität Innsbruck
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funders: OeAD-GmbH, Universität Innsbruck
OpenAlex ID: W7140132488