HiTZ at VarDial 2025 NorSID: Overcoming Data Scarcity with Language Transfer and Automatic Data Annotation

TL;DR

This paper describes a multilingual approach using transfer learning and data annotation techniques to improve dialect identification, intent detection, and slot filling in Norwegian dialects with limited data.

Contribution

It introduces a cross-lingual multitask model for intent detection and slot filling, and analyzes factors affecting performance in dialect identification tasks.

Findings

Models maintained performance across datasets due to domain similarity.

Multitask fine-tuning improved cross-lingual transfer.

Analysis of dataset artifacts and method effectiveness.

Abstract

In this paper we present our submission for the NorSID Shared Task as part of the 2025 VarDial Workshop (Scherrer et al., 2025), consisting of three tasks: Intent Detection, Slot Filling and Dialect Identification, evaluated using data in different dialects of the Norwegian language. For Intent Detection and Slot Filling, we have fine-tuned a multitask model in a cross-lingual setting, to leverage the xSID dataset available in 17 languages. In the case of Dialect Identification, our final submission consists of a model fine-tuned on the provided development set, which has obtained the highest scores within our experiments. Our final results on the test set show that our models do not drop in performance compared to the development set, likely due to the domain-specificity of the dataset and the similar distribution of both subsets. Finally, we also report an in-depth analysis of the provided datasets and their artifacts, as well as other sets of experiments that have been carried out but did not yield the best results. Additionally, we present an analysis on the reasons why some methods have been more successful than others; mainly the impact of the combination of languages and domain-specificity of the training data on the results.