# Robust Language Identification for Romansh Varieties

arXiv: https://arxiv.org/abs/2603.15969 · HTML: https://mlsanity.com/paper/2603.15969/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 16 Mar 2026.

## Builds on (most cited first, 7 of 7)

- [SwissBERT: The Multilingual Language Model for Switzerland](https://mlsanity.com/paper/2303.13310/) (2023)
- [Linguistic Resources for Bhojpuri, Magahi and Maithili: Statistics about them, their Similarity Estimates, and Baselines for Three Applications](https://mlsanity.com/paper/2004.13945/) (2020)
- [Fumbling in Babel: An Investigation into ChatGPT's Language Identification Ability](https://mlsanity.com/paper/2311.09696/) (2023)
- [ConLID: Supervised Contrastive Learning for Low-Resource Language Identification](https://mlsanity.com/paper/2506.15304/) (2025)
- [SIGTYP 2021 Shared Task: Robust Spoken Language Identification](https://mlsanity.com/paper/2106.03895/) (2021)
- [Expanding the WMT24++ Benchmark with Rumantsch Grischun, Sursilvan, Sutsilvan, Surmiran, Puter, and Vallader](https://mlsanity.com/paper/2509.03148/) (2025)
- [The Mediomatix Corpus: Parallel Data for Romansh Language Varieties via Comparable Schoolbooks](https://mlsanity.com/paper/2508.16371/) (2025)

## Built on by (most cited first, 2 of 2)

- [Translation Asymmetry in LLMs as a Data Augmentation Factor: A Case Study for 6 Romansh Language Varieties](https://mlsanity.com/paper/2603.25489/) (2026)
- [RUMLEM: A Dictionary-Based Lemmatizer for Romansh](https://mlsanity.com/paper/2604.11233/) (2026)

## Abstract (arXiv)

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.