# Make Every Letter Count: Building Dialect Variation Dictionaries from Monolingual Corpora

arXiv: https://arxiv.org/abs/2509.17855 · HTML: https://mlsanity.com/paper/2509.17855/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 46 references that are in the map.
- First posted on arXiv 22 Sep 2025.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DIALECTBENCH: A NLP Benchmark for Dialects, Varieties, and Closely-Related Languages](https://mlsanity.com/paper/2403.11009/) (2024)
- [Natural Language Processing for Dialects of a Language: A Survey](https://mlsanity.com/paper/2401.05632/) (2024)
- [A Large-Scale Comparison of Historical Text Normalization Systems](https://mlsanity.com/paper/1904.02036/) (2019)
- [Does Manipulating Tokenization Aid Cross-Lingual Transfer? A Study on POS Tagging for Non-Standardized Languages](https://mlsanity.com/paper/2304.10158/) (2023)
- [A Survey of Corpora for Germanic Low-Resource Languages and Dialects](https://mlsanity.com/paper/2304.09805/) (2023)
- [A Benchmark for Evaluating Machine Translation Metrics on Dialects Without Standard Orthography](https://mlsanity.com/paper/2311.16865/) (2023)
- [Machine Translation of Low-Resource Spoken Dialects: Strategies for Normalizing Swiss German](https://mlsanity.com/paper/1710.11035/) (2017)
- [Sebastian, Basti, Wastl?! Recognizing Named Entities in Bavarian Dialectal Data](https://mlsanity.com/paper/2403.12749/) (2024)
- [Cross-Dialect Information Retrieval: Information Access in Low-Resource and High-Variance Languages](https://mlsanity.com/paper/2412.12806/) (2024)
- [We're Calling an Intervention: Exploring Fundamental Hurdles in Adapting Language Models to Nonstandard Text](https://mlsanity.com/paper/2404.07304/) (2024)
- [A Swiss German Dictionary: Variation in Speech and Writing](https://mlsanity.com/paper/2004.00139/) (2020)
- [Investigating Neural Machine Translation for Low-Resource Languages: Using Bavarian as a Case Study](https://mlsanity.com/paper/2404.08259/) (2024)

## Built on by (most cited first, 3 of 3)

- [Information Asymmetry across Language Varieties: A Case Study on Cantonese-Mandarin and Bavarian-German QA](https://mlsanity.com/paper/2603.14782/) (2026)
- [Meenz bleibt Meenz, but Large Language Models Do Not Speak Its Dialect](https://mlsanity.com/paper/2602.16852/) (2026)
- [Resource-Lean Lexicon Induction for German Dialects](https://mlsanity.com/paper/2604.23824/) (2026)

## Abstract (arXiv)

Dialects exhibit a substantial degree of variation due to the lack of a standard orthography. At the same time, the ability of Large Language Models (LLMs) to process dialects remains largely understudied. To address this gap, we use Bavarian as a case study and investigate the lexical dialect understanding capability of LLMs by examining how well they recognize and translate dialectal terms across different parts-of-speech. To this end, we introduce DiaLemma, a novel annotation framework for creating dialect variation dictionaries from monolingual data only, and use it to compile a ground truth dataset consisting of 100K human-annotated German-Bavarian word pairs. We evaluate how well nine state-of-the-art LLMs can judge Bavarian terms as dialect translations, inflected variants, or unrelated forms of a given German lemma. Our results show that LLMs perform best on nouns and lexically similar word pairs, and struggle most in distinguishing between direct translations and inflected variants. Interestingly, providing additional context in the form of example usages improves the translation performance, but reduces their ability to recognize dialect variants. This study highlights the limitations of LLMs in dealing with orthographic dialect variation and emphasizes the need for future work on adapting LLMs to dialects.