What Do Dialect Speakers Want? A Survey of Attitudes Towards Language Technology for G erman Dialects (2024)

Abstract: 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.

## introduction most natural language processing (nlp) research focuses on languages with many speakers, high degrees of standardization and large amounts of available data @xcite . only recently, more nlp projects have started to include local, non-standardized languages and dialects. however, different speakers and cultures have different needs. as recently echoed by multiple researchers, the creation of language technologies (lts) should take into account what the relevant speaker community finds useful @xcite @xcite , and communities can differ from one another in that regard @xcite . in this work, we focus on dialects and regional languages 1 closely related to german (for the sake of simplicity, we use 'dialects' to refer to these varieties in this paper). with dialect competence generally being in decline in the german-speaking area, 1 our survey also includes responses by speakers of low german, which is officially recognized as a regional language. today, dialect speakers usually also speak standard german, while dialects often are replaced by regiolects -intermediate varieties between standard and dialect @xcite . speaker attitudes towards dialects vary greatly @xcite . although these dialects are predominantly spoken and only few of them have traditional orthographies, many of them are also used in written, digital contexts @xcite . accordingly, some nlp datasets based (primarily) on such digital data exist, and a small number is also annotated for nlp tasks @xcite . several recent publications feature lts for german dialects, such as machine translation @xcite @xcite @xcite , speech-to-text @xcite @xcite ) and text-to-speech systems @xcite , and slot and intent detection for conversational assistants @xcite @xcite . to investigate what speaker communities are interested in, we survey dialect speakers from different german-speaking areas (figure 1 ). we gather a snapshot of their current attitudes towards lts to answer the following questions: q1) which dialect technologies do respondents find especially useful ( §4.2)? q2) does this depend on whether the in-or output is dialectal, and on whether the lt works with speech or text data ( §4.3)? q3) how does this reflect relevant sociolinguistic factors ( §4.4)? ## related work the closest survey to ours on investigating attitudes of speakers of non-standard language varieties towards lts is by @xcite . they conducted a survey on the actual and desired lt use by speakers of different creoles (@xmath0. they find that the needs vary from speaker community to speaker community, and that speakers who are also highly proficient in the local high-prestige language are less interested in creole lts. of the technologies included in the survey, speech-related technologies (transcription and synthesis) are the most popular; machine translation (mt) and question answering software are also desired by multiple communities, while spellcheckers are controversial. @xcite surveyed speakers of four regional european languages foot_0 about whether and why they use (or do not use) their languages in digital contexts. when asked about the desirability of currently unavailable spellcheckers and mt systems, more respondents judged both as desirable than not, although the exact proportions vary across communities. @xcite found similar results in surveys of mauritian creole (@xmath1. @xcite investigate actual lt use by speakers of different european national languages (91-922 respondents per country). the most commonly used lts are mt, search engines and spell-or grammar checkers. when respondents do not use specific lts, this can simply be due to the absence of such technologies for certain languages, but also due to a lack of interest in specific language-lt combinations. recently, several surveys have also investigated speaker community perspectives regarding lts for many different indigenous language communities @xcite @xcite . however, these surveys focus on languages with very different sociolinguistic contexts than the ones in our survey and that are unrelated to their respective local highresource languages. ## questionnaire our questionnaire is aimed at speakers of german dialects and related regional languages and consists of two main parts: we ask our participants about their dialect, and we ask about their opinions on hypothetical lts for their dialect. several of the questions regarding dialect use are inspired by soria et al. (2018) and millour (2020) 2). for each technology, we provide a brief definition to make the survey accessible to a broad audience (e.g., 'speech-to-text systems transcribe spoken language. they are for instance used for automatically generating subtitles or in the context of dictation software.'). we then ask participants to rate on a 5-point likert scale how useful they would find such a tool for their dialect. we allow respondents to elaborate on their answers in comment fields. the full questionnaire is in appendix §a. the questionnaire was written in german, and was estimated to take between 10-15 minutes for completion. 3 it was online for three weeks in september and october 2023 and got disseminated via word of mouth, social media, mailing lists and by contacting dialect and heritage societies. our results are hence based on a convenience sample. ## results we reached 441 people, 327 of whom are selfreported dialect speakers and finished the entire questionnaire -their responses are presented in the following. detailed answer distributions are in appendix §a; correlations are in §b. ## discussion and conclusion we surveyed speakers of dialect varieties on their attitudes towards lts. generally, the survey participants prefer lts working with dialect input rather than output. they also tend to prefer tools that process speech over those for text (q2). this is consistent with chrupała's (2023) argument that nlp should focus more on spoken language to better represent actual language use. it also reflects the complex, often conflicting attitudes speakers of multiple varieties have towards competing linguistic and social norms. consequently, the most popular potential dialect lts (q1) in our survey process spoken dialectal input: virtual assistants with dialect input and speech-to-text systems. however, like @xcite , we find that different speaker communities vary in their attitudes towards lts (q3). for instance, opinions on the standardization of a dialect are a relevant factor regarding the desirability of written lts. nevertheless, the acceptance and rejection of lts is related to individual factors beyond just attitudes, e.g., experience with and trust in digital technology. we hope that our study inspires other nlp researchers to actively consider the wants and needs of the relevant speaker communities. based on the results of this study, we also encourage the dialect nlp community to pursue more work on spoken language processing. dialect they indicated speaking ( §a2), combined with region information for respondents who supplied ambiguous dialect names. for the (other) german, austrian, and swiss respondents, we used region information.