# Machine Translation of Low-Resource Spoken Dialects: Strategies for Normalizing Swiss German

arXiv: https://arxiv.org/abs/1710.11035 · HTML: https://mlsanity.com/paper/1710.11035/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2017.

## Built on by (most cited first, 9 of 9)

- [Natural Language Processing for Dialects of a Language: A Survey](https://mlsanity.com/paper/2401.05632/) (2024)
- [SwissDial: Parallel Multidialectal Corpus of Spoken Swiss German](https://mlsanity.com/paper/2103.11401/) (2021)
- [What Do Dialect Speakers Want? A Survey of Attitudes Towards Language Technology for German Dialects](https://mlsanity.com/paper/2402.11968/) (2024)
- [FRMT: A Benchmark for Few-Shot Region-Aware Machine Translation](https://mlsanity.com/paper/2210.00193/) (2022)
- [CODET: A Benchmark for Contrastive Dialectal Evaluation of Machine Translation](https://mlsanity.com/paper/2305.17267/) (2023)
- [Make Every Letter Count: Building Dialect Variation Dictionaries from Monolingual Corpora](https://mlsanity.com/paper/2509.17855/) (2025)
- [Low-resource Bilingual Dialect Lexicon Induction with Large Language Models](https://mlsanity.com/paper/2304.09957/) (2023)
- [Modeling Orthographic Variation in Occitan's Dialects](https://mlsanity.com/paper/2404.19315/) (2024)
- [Low-Resource NMT: A Case Study on the Written and Spoken Languages in Hong Kong](https://mlsanity.com/paper/2505.17816/) (2025)

## Abstract (arXiv)

The goal of this work is to design a machine translation (MT) system for a low-resource family of dialects, collectively known as Swiss German, which are widely spoken in Switzerland but seldom written. We collected a significant number of parallel written resources to start with, up to a total of about 60k words. Moreover, we identified several other promising data sources for Swiss German. Then, we designed and compared three strategies for normalizing Swiss German input in order to address the regional diversity. We found that character-based neural MT was the best solution for text normalization. In combination with phrase-based statistical MT, our solution reached 36% BLEU score when translating from the Bernese dialect. This value, however, decreases as the testing data becomes more remote from the training one, geographically and topically. These resources and normalization techniques are a first step towards full MT of Swiss German dialects.