# What Do Dialect Speakers Want? A Survey of Attitudes Towards Language Technology for German Dialects

arXiv: https://arxiv.org/abs/2402.11968 · HTML: https://mlsanity.com/paper/2402.11968/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 19 Feb 2024.

## Builds on (most cited first, 11 of 11)

- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding](https://mlsanity.com/paper/2105.07316/) (2021)
- [What a Creole Wants, What a Creole Needs](https://mlsanity.com/paper/2206.00437/) (2022)
- [Ethical Considerations for Machine Translation of Indigenous Languages: Giving a Voice to the Speakers](https://mlsanity.com/paper/2305.19474/) (2023)
- [Not always about you: Prioritizing community needs when developing endangered language technology](https://mlsanity.com/paper/2204.05541/) (2022)
- [Putting Natural in Natural Language Processing](https://mlsanity.com/paper/2305.04572/) (2023)
- [A Survey of Corpora for Germanic Low-Resource Languages and Dialects](https://mlsanity.com/paper/2304.09805/) (2023)
- ["It's how you do things that matters": Attending to Process to Better Serve Indigenous Communities with Language Technologies](https://mlsanity.com/paper/2402.02639/) (2024)
- [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)
- [Designing Language Technologies for Social Good: The Road not Taken](https://mlsanity.com/paper/2110.07444/) (2021)

## Built on by (most cited first, 21 of 21)

- [Culture is Not Trivia: Sociocultural Theory for Cultural NLP](https://mlsanity.com/paper/2502.12057/) (2025)
- [Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art](https://mlsanity.com/paper/2406.03930/) (2024)
- [The AI Gap: How Socioeconomic Status Affects Language Technology Interactions](https://mlsanity.com/paper/2505.12158/) (2025)
- [A Multi-Dialectal Dataset for German Dialect ASR and Dialect-to-Standard Speech Translation](https://mlsanity.com/paper/2506.02894/) (2025)
- [LLM Alignment for the Arabs: A Homogenous Culture or Diverse Ones?](https://mlsanity.com/paper/2503.15003/) (2025)
- [Low-resource Machine Translation: what for? who for? An observational study on a dedicated Tetun language translation service](https://mlsanity.com/paper/2411.12262/) (2024)
- [Building Better: Avoiding Pitfalls in Developing Language Resources when Data is Scarce](https://mlsanity.com/paper/2410.12691/) (2024)
- [Improving Dialectal Slot and Intent Detection with Auxiliary Tasks: A Multi-Dialectal Bavarian Case Study](https://mlsanity.com/paper/2501.03863/) (2025)
- [What Do Indonesians Really Need from Language Technology? A Nationwide Survey](https://mlsanity.com/paper/2506.07506/) (2025)
- [Reassessing Active Learning Adoption in Contemporary NLP: A Community Survey](https://mlsanity.com/paper/2503.09701/) (2025)
- [Dialect Normalization using Large Language Models and Morphological Rules](https://mlsanity.com/paper/2506.08907/) (2025)
- [Standard-to-Dialect Transfer Trends Differ across Text and Speech: A Case Study on Intent and Topic Classification in German Dialects](https://mlsanity.com/paper/2510.07890/) (2025)
- [Indirect Question Answering in English, German and Bavarian: A Challenging Task for High- and Low-Resource Languages Alike](https://mlsanity.com/paper/2603.15130/) (2026)
- [Beyond Accuracy: Community Perspectives on Machine Translation](https://mlsanity.com/paper/2606.09655/) (2026)
- [Deconstructing Jazz Piano Style Using Machine Learning](https://mlsanity.com/paper/2504.05009/) (2025)
- [Cross-Lingual Representation Alignment Through Contrastive Image-Caption Tuning](https://mlsanity.com/paper/2505.13628/) (2025)
- [textitVersteasch du mi? Computational and Socio-Linguistic Perspectives on GenAI, LLMs, and Non-Standard Language](https://mlsanity.com/paper/2603.28213/) (2026)
- [Saar-Voice: A Multi-Speaker Saarbr"ucken Dialect Speech Corpus](https://mlsanity.com/paper/2604.11803/) (2026)
- [Harnessing Linguistic Dissimilarity for Language Generalization on Unseen Low-Resource Varieties](https://mlsanity.com/paper/2605.04500/) (2026)
- [Romanized Arabic Across Dialects: Views, Usage Patterns, and Linguistic Variation](https://mlsanity.com/paper/2608.02555/) (2026)
- [Towards the Roof of the World: NLP Progress in Tibetan Languages](https://mlsanity.com/paper/2610.13398/) (2026)

## Abstract (arXiv)

Natural language processing (NLP) has largely focused on modelling standardized languages. More recently, attention has increasingly shifted to local, non-standardized languages and dialects. However, the relevant speaker populations' needs and wishes with respect to NLP tools are largely unknown. In this paper, we focus on dialects and regional languages related to German -- a group of varieties that is heterogeneous in terms of prestige and standardization. We survey speakers of these varieties (N=327) and present their opinions on hypothetical language technologies for their dialects. Although attitudes vary among subgroups of our respondents, we find that respondents are especially in favour of potential NLP tools that work with dialectal input (especially audio input) such as virtual assistants, and less so for applications that produce dialectal output such as machine translation or spellcheckers.