Robust Language Identification for Romansh Varieties

2026-03-16

AI Summary

This study addresses the challenge of effectively distinguishing between the multiple regional dialects of Romansh and its supra-regional standard variety, Rumantsch Grischun—a task that existing language identification systems struggle to perform accurately. To this end, the work proposes a support vector machine (SVM)-based approach for Romansh dialect identification and introduces, for the first time, a benchmark dataset encompassing both dialectal and Rumantsch Grischun texts across two distinct textual domains. Experimental results demonstrate that the proposed system achieves an average accuracy of 97% in in-domain evaluations, substantially advancing downstream applications such as dialect-aware spell checking and machine translation.

Technology Category

• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Language and Vision

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

The Romansh language has several regional varieties, called idioms, which sometimes have limited mutual intelligibility. Despite this linguistic diversity, there has been a lack of documented efforts to build a language identification (LID) system that can distinguish between these idioms. Since Romansh LID should also be able to recognize Rumantsch Grischun, a supra-regional variety that combines elements of several idioms, this makes for a novel and interesting classification problem. In this paper, we present a LID system for Romansh idioms based on an SVM approach. We evaluate our model on a newly curated benchmark across two domains and find that it reaches an average in-domain accuracy of 97%, enabling applications such as idiom-aware spell checking or machine translation. Our classifier is publicly available.

Problem

• Language Identification
• Romansh
• Idioms
• Rumantsch Grischun
• Linguistic Diversity

Innovation

• Language Identification
• Romansh idioms
• SVM classifier
• Rumantsch Grischun
• dialect-aware NLP