mlsanityData to 7 Oct 2026

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

arXiv 2402.11968 ↗First posted 19 Feb 2024cs.CLcited by 20

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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.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 11 of its 37 references that are in the map. First posted on arXiv 19 Feb 2024.

Builds on

11 of 37 references.

References · 11

  1. 2020The State and Fate of Linguistic Diversity and Inclusion in the NLP Worldcited by 789
  2. 2021From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understandingcited by 43
  3. 2022What a Creole Wants, What a Creole Needscited by 27
  4. 2023Ethical Considerations for Machine Translation of Indigenous Languages: Giving a Voice to the Speakerscited by 26
  5. 2022Not always about you: Prioritizing community needs when developing endangered language technologycited by 21
  6. 2023Putting Natural in Natural Language Processingcited by 17
  7. 2023A Survey of Corpora for Germanic Low-Resource Languages and Dialectscited by 16
  8. 2024"It's how you do things that matters": Attending to Process to Better Serve Indigenous Communities with Language Technologiescited by 12
  9. 2023A Benchmark for Evaluating Machine Translation Metrics on Dialects Without Standard Orthographycited by 11
  10. 2017Machine Translation of Low-Resource Spoken Dialects: Strategies for Normalizing Swiss Germancited by 9
  11. 2021Designing Language Technologies for Social Good: The Road not Takencited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 11; 2026: 6.

20 papers.

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  1. 2025Culture is Not Trivia: Sociocultural Theory for Cultural NLPcited by 40
  2. 2024Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Artcited by 26
  3. 2025The AI Gap: How Socioeconomic Status Affects Language Technology Interactionscited by 15
  4. 2025A Multi-Dialectal Dataset for German Dialect ASR and Dialect-to-Standard Speech Translationcited by 7
  5. 2025LLM Alignment for the Arabs: A Homogenous Culture or Diverse Ones?cited by 6
  6. 2024Low-resource Machine Translation: what for? who for? An observational study on a dedicated Tetun language translation servicecited by 5
  7. 2024Building Better: Avoiding Pitfalls in Developing Language Resources when Data is Scarcecited by 4
  8. 2025Improving Dialectal Slot and Intent Detection with Auxiliary Tasks: A Multi-Dialectal Bavarian Case Studycited by 3
  9. 2025Reassessing Active Learning Adoption in Contemporary NLP: A Community Surveycited by 2
  10. 2025What Do Indonesians Really Need from Language Technology? A Nationwide Surveycited by 2
  11. 2025Dialect Normalization using Large Language Models and Morphological Rulescited by 2
  12. 2025Standard-to-Dialect Transfer Trends Differ across Text and Speech: A Case Study on Intent and Topic Classification in German Dialectscited by 2
8 more
  1. 2026Indirect Question Answering in English, German and Bavarian: A Challenging Task for High- and Low-Resource Languages Alikecited by 1
  2. 2026Beyond Accuracy: Community Perspectives on Machine Translationcited by 1
  3. 2025Deconstructing Jazz Piano Style Using Machine Learningcited by 0
  4. 2025Cross-Lingual Representation Alignment Through Contrastive Image-Caption Tuningcited by 0
  5. 2026textitVersteasch du mi? Computational and Socio-Linguistic Perspectives on GenAI, LLMs, and Non-Standard Languagecited by 0
  6. 2026Saar-Voice: A Multi-Speaker Saarbr"ucken Dialect Speech Corpuscited by 0
  7. 2026Harnessing Linguistic Dissimilarity for Language Generalization on Unseen Low-Resource Varietiescited by 0
  8. 2026Romanized Arabic Across Dialects: Views, Usage Patterns, and Linguistic Variationcited by 0