Question:
Which of the following statements about German dialect speakers' attitudes toward hypothetical language technologies (LTs) are supported by the survey findings?
A. Dialect speakers strongly favor technologies that process dialectal input, such as virtual assistants responding to spoken commands in their dialect.
B. Speech-to-text systems that transcribe dialectal audio into written Standard German are more preferred than those producing written dialect output.
C. Machine translation tools that convert dialect texts into foreign languages are viewed as equally useful as those translating into Standard German.
D. Spellcheckers for dialects are widely opposed due to concerns about enforcing arbitrary standardized orthographies.

Answer: AD

Virtual assistants and chatbots
The most clearly favoured LT by dialect speakers in our survey are virtual assistants (such as Siri or Alexa) that can respond to dialectal input (71% in favour, 20% against). Chatbots that can handle dialectal input are less popular, but still deemed useful by a slight majority (52%). Systems that could output dialectal responses are less popular: 48% would find virtual assistants that answer in dialect useful, and 34% think so about chatbots.
Speech-to-text and text-to-speech
When asked about speech-to-text (STT) software, a majority (61%) is in favour of systems that transcribe spoken dialect into written Standard German, and a slightly smaller majority (58%) is in favour of written dialectal output. When it comes to text-to-speech (TTS) systems that synthesize dialect text into a spoken form, the respondents are even more split, with 47% in favour and 35% against.
Machine translation
We ask for opinions on four different configurations regarding automatic translation of written texts: each possible combination for translation into vs. out of the dialect and from/into Standard German vs. a foreign language. All options are to some degree controversial among the respondents, with translation from the dialect into Standard German being the most popular (52% in favour) and from the dialect into a foreign language the least popular (25% in favour).
Spellcheckers
Most respondents (59%) are opposed to spell- or grammar checkers for their dialect, although a quarter (25%) is in favour. Several respondents mention opposition to spellcheckers since they want their dialectal writing to exactly reflect the pronunciation and word choices of their local dialect and would be bothered if a spellchecker changed them to an arbitrary standardized version of the dialect.
As seen in the previous section, there is a general tendency to prefer versions of LTs that process dialectal input rather than produce dialectal output (Q2). Several write-in comments voice worries about dialectal output not modelling their dialect accurately enough. Additionally, technologies dealing with spoken language tend to be rated more positively than those focusing on text only.
Correlation with attitudes towards orthography
Being in favour of a standardized dialect orthography is positively, albeit not very strongly, correlated with being in favour of any technology involving a written version of the dialect and/or (written or spoken) dialectal output (Spearman’s ρ values between 0.14 and 0.47 per LT with p-values <0.001).

Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 823–841
August 11-16, 2024 ©2024 Association for Computational Linguistics
What Do Dialect Speakers Want?
A Survey of Attitudes Towards Language Technology for German Dialects
Verena BlaschkeÆ
U
Christoph Purschke×
Hinrich SchützeÆ
U
Barbara PlankÆ☼
U
U Center for Information and Language Processing (CIS), LMU Munich, Germany
Æ Munich Center for Machine Learning (MCML), Munich, Germany
× Department of Humanities, University of Luxembourg, Luxembourg
☼Department of Computer Science, IT University of Copenhagen, Denmark
{verena.blaschke, b.plank}@lmu.de
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 stan-
dardization. We survey speakers of these va-
rieties (N=327) and present their opinions on
hypothetical language technologies for their
dialects. Although attitudes vary among sub-
groups of our respondents, we find that respon-
dents 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.
1
Introduction
Most natural language processing (NLP) research
focuses on languages with many speakers, high
degrees of standardization and large amounts of
available data (Joshi et al., 2020). 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 (Bird, 2020, 2022; Liu et al., 2022;
Mukhija et al., 2021), and communities can differ
from one another in that regard (Lent et al., 2022).
In this work, we focus on dialects and regional
languages1 closely related to German (for the sake
of simplicity, we use ‘dialects’ to refer to these vari-
eties in this paper). With dialect competence gener-
ally being in decline in the German-speaking area,
1Our survey also includes responses by speakers of Low
German, which is officially recognized as a regional language.
Figure 1: Countries and German states in which the
respondents’ dialects are spoken, with the number of
respective respondents, and the overall age distribution.
today, dialect speakers usually also speak Standard
German, while dialects often are replaced by re-
giolects – intermediate varieties between standard
and dialect (Kehrein, 2019). Speaker attitudes to-
wards dialects vary greatly (Gärtig et al., 2010,
pp. 155–167).
Although these dialects are predominantly spo-
ken and only few of them have traditional orthogra-
phies, many of them are also used in written, digital
contexts (Androutsopoulos, 2003). Accordingly,
some NLP datasets based (primarily) on such dig-
ital data exist, and a small number is also anno-
tated for NLP tasks (Blaschke et al., 2023). Sev-
eral recent publications feature LTs for German di-
alects, such as machine translation (Haddow et al.,
2013; Honnet et al., 2018; Lambrecht et al., 2022;
Aepli et al., 2023a; Her and Kruschwitz, 2024),
speech-to-text (Herms et al., 2016; Nigmatulina
et al., 2020; Gerlach et al., 2022) and text-to-speech
systems (Gutscher et al., 2023), and slot and in-
tent detection for conversational assistants (van der
Goot et al., 2021; Aepli et al., 2023b; Winkler et al.,
2024; Abboud and Oz, 2024).
To investigate what speaker communities are in-
terested in, we survey dialect speakers from differ-
ent German-speaking areas (Figure 1). We gather
823
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)?
2
Related Work
The closest survey to ours on investigating attitudes
of speakers of non-standard language varieties to-
wards LTs is by Lent et al. (2022). They conducted
a survey on the actual and desired LT use by speak-
ers of different creoles (N=37). 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.
Soria et al. (2018) surveyed speakers of four
regional European languages2 about whether and
why they use (or do not use) their languages in
digital contexts. When asked about the desirabil-
ity of currently unavailable spellcheckers and MT
systems, more respondents judged both as desir-
able than not, although the exact proportions vary
across communities. Millour (2019; 2020, pp. 230,
239) found similar results in surveys of Mauritian
Creole (N=144) and Alsatian speakers (N=1,224).
Conversely, Way et al. (2022) 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 lan-
guages, 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
(Mager et al., 2023; Cooper et al., 2024; Dolinska
et al., 2024; Tonja et al., 2024). However, these sur-
veys focus on languages with very different socio-
linguistic contexts than the ones in our survey and
2Karelian (N=156, Salonen, 2017), Breton (N=202, Hicks,
2017), Basque (N=427, Gurrutxaga Hernaiz and Cebe-
rio Berger, 2017), Sardinian (N=516, Russo and Soria, 2017).
that are unrelated to their respective local high-
resource languages.
3
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 So-
ria et al. (2018) and Millour (2020), and we choose
a similar selection of LTs as Way et al. (2022)
(§4.2). 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 re-
spondents 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.
4
Results
We reached 441 people, 327 of whom are self-
reported 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.
4.1
Dialect Background and Attitudes
Most of our respondents answer that they have a
very good command of their dialect (68%) and
acquired it as a mother tongue (71%). Figure 1
shows where the respondents’ dialects are spoken
(and their age distribution): mostly in Germany
(72%), followed by Switzerland (14%) and Aus-
tria (6%).4 Nearly a quarter (24%) each are in
380% of our respondents took <15 min to fill out the entire
questionnaire, and 60% even less than 10 min.
4The other varieties are spoken in areas with minority
speaker communities: Italy (Bavarian), France (Alemannic),
the Netherlands (Low German), and Romania. The geographic
distribution of our respondents is not representative of the
overall dialect speaker population.
824
their twenties and thirties, almost all others are
older. When rating how traditional their dialect is
on a scale from 1 (traditional dialect that people
from other regions have trouble understanding) to
5 (regionally marked German easily understood by
outsiders), the largest group of respondents (35%)
indicated a 2 (µ=2.6, σ=1.1).
Just over half of our respondents (52%) speak
their dialect on a daily basis, and 43% indicate
that they would like to use their dialect in all ar-
eas of life. Most respondents (70%) value the di-
versity of their dialect. Nearly two thirds (65%)
are opposed to having a standardized orthography
for their dialect. Just over half of the respondents
(53%) say that their dialect is only spoken and not
well-suited for written communication – neverthe-
less, two thirds (66%) also write their dialect, even
if rarely. Many (63%) find it easy to read dialectal
texts written by others. Written dialect is com-
monly used for communicating with others – the
most common writing scenarios are text messages
(57%, multiple responses possible), followed by
letters/emails (26%), social media posts and com-
ments (19% each) – but also for notes to oneself
including diary entries (19%).
About a third (35%) indicate that they are ac-
tively engaged in dialect preservation pursuits (mul-
tiple responses possible): 13% as members of di-
alect preservation societies, 4% as teachers, and
22% in other ways. Write-in comments by the last
group point out other language-related professions,
but also include speaking the dialect in public or
with children as a means of active dialect preser-
vation.5 We compare the opinions of respondents
with and without such dialect engagement in §4.4.
14% of our respondents are familiar with at least
one LT that already caters to their dialect. Just
over half of the respondents (54%) indicate that
their dialect being represented by more LTs would
make it more attractive to younger generations, and
a smaller group (31%) says they would use their
dialect more often given suitable LTs.
5The write-in answers contain 13 mentions of speaking the
dialect with family members (especially children and grand-
children), 18 mentions of simply speaking the dialect (in pub-
lic), 14 mentions of carrying out dialect-related research (as
a job or a hobby), and 10 mentions of using the dialect in
the context of literature or music, with slight overlap between
these groups. None of these subgroups are concentrated in
any specific area, but instead include respondents from ar-
eas where dialects and regional languages have very different
statuses (cf. §4.4): Low German speakers as well as other
German respondents, Swiss respondents, respondents from
countries where German is a minority language, and so on.
0
20
40
60
80
100 %
Spell-
checkers
Search
engines
MT
oth→dial
MT
deu→dial
MT
dial→oth
MT
dial→deu
TTS
STT dial
STT deu
Chatbot
output
Assistant
output
Chatbot
input
Assistant
input
10
17
9
15
14
19
17
27
25
12
19
23
33
15
26
17
22
22
33
30
31
36
22
29
30
38
13
13
12
10
13
12
14
9
11
18
15
16
8
3
2
2
2
2
1
4
2
2
2
1
2
1
23
19
27
21
21
16
17
16
12
22
19
14
11
36
23
33
30
28
19
18
16
14
24
17
16
9
Useless
Cannot judge
Rather useful
Rather useless
Neither/nor
Useful
100
80
60
40
20
0 %
Figure 2: Opinions on potential language technolo-
gies for dialects. STT=speech-to-text, TTS=text-to-
speech, dial=dialect, deu=German, oth=other languages,
MT=machine translation, cannot judge=skip question.
4.2
Which dialect LTs are deemed useful?
Figure 2 shows our respondents’ opinions on LTs
(Q1), and Appendix §C presents the average scores
per LT when responses are mapped to a numer-
ical scale.
While there are diverging opinions
on every LT – there is no single technology that
(nearly) all respondents consider useful or useless
for their dialect – some trends emerge, as we dis-
cuss next.6 Overall, the responses are generally cor-
related with each other: respondents who think pos-
itively/negatively of one technology tend to think
similarly about others. Nevertheless, some LTs are
overall more popular, and some less so:
Virtual assistants and chatbots
The most
clearly favoured LT by dialect speakers in our sur-
vey are virtual assistants (such as Siri or Alexa)
that can respond to dialectal input (71% in favour,
20% against). Chatbots that can handle dialectal
input are less popular, but still deemed useful by a
slight majority (52%). Systems that could output
dialectal responses are less popular: 48% would
6Of the technologies we included, MT, search engines and
spell checkers are the most used LTs in the EU (Way et al.,
2022, p. 26). We assume that the tools that people use a
lot are also tools they generally deem useful, yet those tools
are all ranked relatively low in our results – suggesting that
our results reveal attitudes on dialect LTs rather than LTs in
general.
825
find virtual assistants that answer in dialect useful,
and 34% think so about chatbots.
Speech-to-text and text-to-speech
When asked
about speech-to-text (STT) software, a majority
(61%) is in favour of systems that transcribe spoken
dialect into written Standard German, and a slightly
smaller majority (58%) is in favour of written di-
alectal output. When it comes to text-to-speech
(TTS) systems that synthesize dialect text into a
spoken form, the respondents are even more split,
with 47% in favour and 35% against.
Machine translation
We ask for opinions on
four different configurations regarding automatic
translation of written texts: each possible combina-
tion for translation into vs. out of the dialect and
from/into Standard German vs. a foreign language.
All options are to some degree controversial among
the respondents, with translation from the dialect
into Standard German being the most popular (52%
in favour) and from the dialect into a foreign lan-
guage the least popular (25% in favour).
Search engines
Search engines that could deal
with dialectal input are controversial, with 43%
each in favour of and against this LT, although
the negative group holds stronger opinions. Some
write-in comments question whether (monolingual)
information retrieval would produce useful results
or mention finding it easier to write in Standard
German rather than in a dialect, but others voice a
desire to be able to find results for queries including
dialectal terms with no direct German equivalent.
Spellcheckers
Most respondents (59%) are op-
posed to spell- or grammar checkers for their di-
alect, although a quarter (25%) is in favour. Several
respondents mention opposition to spellcheckers
since they want their dialectal writing to exactly re-
flect the pronunciation and word choices of their lo-
cal dialect and would be bothered if a spellchecker
changed them to an arbitrary standardized version
of the dialect.
4.3
Are there differences for dialect input vs.
output and text vs. speech?
As seen in the previous section, there is a general
tendency to prefer versions of LTs that process di-
alectal input rather than produce dialectal output
(Q2). Several write-in comments voice worries
about dialectal output not modelling their dialect
accurately enough. Additionally, technologies deal-
ing with spoken language tend to be rated more
positively than those focusing on text only.
Correlation with attitudes towards orthography
Being in favour of a standardized dialect orthogra-
phy is positively, albeit not very strongly, correlated
with being in favour of any technology involving a
written version of the dialect and/or (written or spo-
ken) dialectal output (Spearman’s ρ values between
0.14 and 0.47 per LT with p-values <0.001).
4.4
Do attitudes reflect sociolinguistic factors?
To address Q3 and the heterogeneity of our respon-
dent group, we compare answers between larger
subgroups. We summarize the results of t-tests
with p-values <0.05. Appendix §D provides more
details, together with two additional comparisons
that only have small effect sizes (speaker age and
dialect traditionality).
Language activists
Since language activists
might have overly enthusiastic attitudes compared
to the speaker population at large (Soria et al.,
2018), we compare those who report involvement
in dialect preservation (‘activists’, N=115) to those
who do not (N=212). Activists are generally more
in favour of LTs for dialects, with statistically sig-
nificant differences for (any kind of) machine trans-
lation, TTS software, spellcheckers, and search en-
gines, as well as for written dialect output options
for STT, chatbots and virtual assistants. Remov-
ing the activists’ responses from our analysis only
barely changes the order of preferred LTs (§C).
Region
Additionally, we compare three large re-
gional subgroups with different sociolinguistic re-
alities. In Germany and Austria, traditional dialects
have been partially replaced by more standard-
like regiolects, while dialects have high prestige
in Switzerland where Standard German is often
reserved for writing (Kehrein, 2019; Ender and
Kaiser, 2009). Low German, traditionally spo-
ken in parts of Northern Germany and the Eastern
Netherlands, is officially recognized as a language
and is more distantly related to Standard German
than the other varieties our participants speak. Its
speaker numbers are in decline, but many Northern
Germans think Low German should receive more
support in, e.g., public schools (Adler et al., 2016).
We compare the opinions of Swiss (N=46) and
Low German (N=58) respondents to German and
Austrian non-Low-German speakers (N=191).7
7We identify the Low German respondents based on the
826
Our Low German respondents are more in favour
of a standardized orthography and of spellcheckers
than our other German and Austrian respondents,
the Swiss respondents less so. This is unsurprising
in that several orthographies have been proposed
for Low German, whereas (typically spoken) di-
alects and (typically written) Standard German ex-
ist in a diglossic state in Switzerland. Nevertheless,
both groups are more in favour of STT software
with dialectal output. The Low German respon-
dents are more in favour of chatbots with dialectal
answers, TTS, (any kind of) MT and search en-
gines. Swiss Germans rate virtual assistants that
can handle dialectal input as more desirable (87%
in favour), and are more in favour of STT software
with Standard German output.
5
Discussion and Conclusion
We surveyed speakers of dialect varieties on their
attitudes towards LTs. Generally, the survey partic-
ipants 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 bet-
ter represent actual language use. It also reflects
the complex, often conflicting attitudes speakers
of multiple varieties have towards competing lin-
guistic 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 Lent et al. (2022), we find that dif-
ferent 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. Neverthe-
less, the acceptance and rejection of LTs is related
to individual factors beyond just attitudes, e.g., ex-
perience with and trust in digital technology.
We hope that our study inspires other NLP re-
searchers 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 respon-
dents, we used region information.
Ethical Considerations
We only collected responses from participants who
consented to having their data saved and analyzed
for research purposes. We did not ask for person-
ally identifying information. We store responses
on a local server and only share results based on
aggregate analyses. Appendix §A contains the full
questionnaire including the introduction where we
describe the purpose of the study and explain what
data we collect and how we use the data.
Participation was completely anonymous, volun-
tary and with no external reward. We do not see
any particular risks associated with this work.
Limitations
Our results are based on a convenience sample; nei-
ther the geographic or age distribution are represen-
tative of the population at large (dialect-speaking
or not). Language activists are over-represented
(hence our additional analysis in §4.4 and Appen-
dices §C and §D), and participating in the survey
may have been especially of interest to people who
feel (in one way or another) strongly about the topic
of dialects and technology. Even so, our respon-
dents are not a monolith in their opinions and we
can see meaningful differences between the relative
popularity of different technologies.
We aimed to keep participation effort low and
therefore limited the number of questions we in-
cluded. We considered asking “Would you use X
if it existed?” in addition to “Would you find X
useful?” to explicitly disentangle the participants’
own needs from what are possibly the perceived
needs of the community. We decided against this in
order to keep the questionnaire as short as possible
and because we were unsure how accurate such
assessment would be.
The scale of our answer possibilities uses “not
useful” as the opposite of “useful.” However, it
would be interesting to instead use a scale from
“harmful” to “useful” in future surveys, in order to
get a better impression of whether respondents who
deem an LT useless in our version of the survey
find it actively harmful or merely uninteresting.
To minimize the total time needed to fill out the
questionnaire and to guarantee the privacy of the
respondents after asking respondents about what
specific dialect they speak (used later to identify
the Low German speakers), we intentionally kept
additional demographic questions at a minimum
and did not ask about education, income, gender,
827
or similar variables.
As this survey is based on self-reporting, we
expect discrepancies between reported and actual
opinions and behaviour. Since participation was
anonymous and entirely voluntary with no external
reward, we think it unlikely for participants to lie
about their opinions. It is likely, though, that (espe-
cially younger) participants overstate their dialect
competence or the traditionality of their dialect,
in line with overall dialect dynamics in German
(Purschke, 2011).
Acknowledgements
We thank everybody who participated in or shared
the survey.
We also thank Yves Scherrer and
Frauke Kreuter for their advice, and the MaiNLP
and CIS members as well as the anonymous review-
ers for their feedback.
This research is supported by the ERC Consol-
idator Grant DIALECT 101043235. We also grate-
fully acknowledge partial funding by the European
Research Council (ERC #740516).
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A
Questionnaire
In this section, we reproduce the questions and an-
swers from our survey, in the original wording as
well as in translation. Translations are in grey ital-
ics, remarks about the questionnaire are in black
italics. Answer options that end with a colon (:)
came with an optional text input field in the ques-
tionnaire. All questions except for the first two
could be skipped without answering.
Herzlich willkommen, servus, grüezi & moin!
Sprachtechnologie ist momentan allgegenwär-
tig, ob bei Übersetzungsprogrammen, Chatbots
oder anderen Anwendungen. Hauptsächlich unter-
stützen diese Anwendungen lediglich Standardspra-
chen – was nicht unbedingt dem entspricht, wie wir
im Alltag Sprache verwenden.
Daher möchten wir herausfinden, wie Sie als
Sprecher*innen von Dialekten und Regionalspra-
chen möglicher Sprachtechnologie für Ihre Sprach-
form gegenüberstehen: welche Anwendungen hal-
ten Sie für wünschenswert bzw. unnötig?
Welcome and hello [in different dialects]!
Language technology is currently omnipresent
– be it in the context of translation software, chat-
bots or other applications. Such applications pri-
marily support standard languages – which is not
necessarily how we use language in our everyday
lives.
Because of this we would like to find out what
you as speakers of dialects and regional languages
think of potential technologies for your language
variety: which applications do you find desirable
or useless?
Das Ausfüllen des Fragebogens dauert etwa 10–15
Minuten.
Wir behandeln Ihre Antworten vertraulich und
veröffentlichen diese nur in anonymisierter Form
und ohne dass Rückschlüsse auf Ihre Person gezo-
gen werden können.
Genauere Details:
Ziel der Befragung ist es zum einen, heraus-
zufinden, ob es Unterschiede zwischen den Ar-
ten von Sprachtechnologien gibt, die Dialektspre-
cher*innen tendenziell als nützlich bzw. nutzlos
bewerten. Zum anderen möchten wir herausfinden,
ob ein statistischer Zusammenhang zwischen die-
sen Antworten und dem Dialekthintergrund und
-gebrauch der Befragten besteht.
Die Antworten werden auf einem Server der
LMU in München gespeichert. Wir speichern da-
bei nur Ihre Antworten und den Antwortzeitpunkt
(um die typische Ausfülldauer besser einzuschät-
zen), nicht aber Ihre IP-Adresse. Wir geben die
Daten nicht an Dritte weiter, sondern veröffentli-
chen lediglich Ergebnisse, die auf Aggregatdaten
und statistischen Analysen beruhen. Zudem zitie-
ren wir gegebenenfalls aus (optional gegebenen)
Kommentarfeld-Antworten.
830
Kontaktmöglichkeit bei Fragen oder Kommen-
taren zu dieser Umfrage: [Contact data of first au-
thor].
Vielen Dank für Ihre Teilnahme!
This questionnaire takes about 10–15 minutes to
fill out.
We treat your answers as confidential and only
share them as anonymized data that do not allow
drawing any inferences about your identity.
More detailed information:
The goal of this survey is firstly to determine
whether there are differences between the types of
language technologies that dialect speakers tend to
find useful or useless. Additionally, we would like
to find out whether there is a statistical correlation
between the answers and the dialect background of
the participants.
We store the answers on an LMU server in Mu-
nich. This only includes storing your answers and
the time of the questions are answered (to better
estimate the typical response duration), but not
your IP address. We do not share your data with
third parties, but only share results based on aggre-
gated data and statistical analyses. Additionally,
we might cite (optional) write-in answers from com-
ment fields.
Contact person in case of questions about or
comments on this study: [Contact data of first au-
thor].
Thank you very much for participating!
□Ich stimme zu, dass meine Antworten wie
oben beschrieben zu Forschungszwecken ge-
speichert und ausgewertet werden. I consent
to my answers being stored and analyzed for
research purposes as outlined above.
The survey only progresses if this box is checked.
In dieser Umfrage untersuchen wir Sprachen
und
Sprachformen,
die
sich
deutlich
vom
Hochdeutschen unterscheiden. Damit meinen wir
mit dem Deutschen verwandte Regionalsprachen
sowie Dialekte, Mundarten und Platt-Varianten,
die meist für eine kleine Region typisch sind
und von Außenstehenden nicht ohne Weiteres ver-
standen werden können. Ein paar Beispiele dafür
sind das Eifeler Platt, Allgäuerisch, Bairisch oder
Nordfriesisch. Der Einfachheit halber verwen-
den wir im Folgenden „Dialekt“ als Sammelbe-
griff für all diese Sprachformen. In this survey,
we focus on languages and language varieties that
are clearly distinct from Standard German. To be
precise, we are interested in regional languages
related to German as well as dialects8 that usually
are typical for a small region and cannot easily
be understood by outsiders. Some examples are
Eifelplatt, Allgäu dialects, Bavarian and North
Frisian. For the sake of simplicity, we will use
“dialect” as umbrella term for all of these language
varieties in the following.
This introduction is partially based on the one
from the REDE project surveys (Schmidt et al.,
2020–).
1. Können Sie einen deutschen Dialekt spre-
chen? Can you speak a German dialect?
Ja, sehr sicher
Yes, very well
223
Ja, gut
Yes, well
71
Ein wenig
A bit
33
Nein
No
16
The 16 respondents who answered ‘no’ are ex-
cluded from the analysis. The survey automatically
ended for them, showing the message: “Alle weit-
eren Fragen richten sich nur an SprecherInnen eines
deutschen Dialekts bzw. einer mit dem Deutschen
nahe verwandten Regionalsprache. Vielen Dank
für Ihre Teilnahme!” “All further questions are
only for speakers of a German dialect or a regional
language closely related to German. Thank you for
participating!”
2. Um welchen Dialekt handelt es sich? Which
dialect specifically?
327 write-in answers.
3. Wann haben Sie diesen Dialekt gelernt? When
did you learn this dialect?
Als Muttersprache
As mother tongue
231
Kindheit/Jugend
Childhood/youth
84
Später
Later
12
4. In welchem Land befindet sich der Ort, an
dem Ihr Dialekt gesprochen wird (z.B. Ihr Hei-
matort)? In which country is the location where
your dialect is spoken (e.g., your hometown)?
See Figure 1.
8In the German version, we include different terms that all
translate to “dialect” but are used in different regions.
831
5. In welchem Bundesland befindet sich dieser
Ort? In which German state is this location?
Only asked if the previous answer is ‘Germany’.
See Figure 1.
6. Wie sehr entspricht Ihr Dialekt dem traditio-
nellen Dialekt des Ortes? How much does your
dialect resemble the traditional dialect of this loca-
tion?
1 – Mein Dialekt ist
sehr traditionell...*
57
2
114
3
93
4
38
5 – Mein Dialekt ist eher
regional gefärbtes...**
25
*1 – Mein Dialekt ist sehr traditionell und für Außenstehende
aus anderen Regionen sehr schwer zu verstehen. 1 – My dialect is
very traditional and very hard to understand for outsiders from other
regions.
**5 – Mein Dialekt ist eher regional gefärbtes Deutsch, das auch
von Außenstehenden recht einfach verstanden wird. 5 – My dialect is
more like regionally marked German that is relatively easily under-
stood by outsiders.
7. Wie häufig sprechen Sie Ihren Dialekt? How
often do you speak your dialect?
The answer options are based on those in the
surveys summarized by Soria et al. (2018).
Täglich
Daily
170
Mehrmals pro Woche
Multiple times per week
48
Min. einmal pro Woche
At least once a week 32
Min. einmal pro Monat
At least once a month
29
Seltener
More rarely
46
Nie
Never
2
8. Schreiben Sie manchmal Ihren Dialekt? Do
you ever write your dialect?
This question and the next one are modelled after
questions by Millour (2020, pp. 228, 237–238).
Ja (egal ob häufig o. selten)
Yes (whether often or rarely)
217
Nein, ich weiß nicht wie
No, I don’t know how
14
Nein, ich habe dazu
keine Gelegenheit*
26
Nein, mein Dialekt ist eine
gesprochene Sprachform...**
62
Nein, aus anderen Gründen:
No, for other reasons:
8
*No, I don’t have any opportunity for this
**Nein, mein Dialekt ist eine gesprochene Sprachform und ich
möchte ihn nicht schreiben No, my dialect is a spoken form of lan-
guage and I don’t want to write it
9. Was schreiben Sie in Ihrem Dialekt? (Mehr-
fachantworten möglich) What do you write in your
dialect? (Multiple answers possible)
Only asked if previous is ‘yes’. 217 participants
responded:
Nachrichten in
Chatprogrammen...*
187
Briefe, Emails
Letters, emails
86
Einträge auf sozialen Medien
Social media posts
63
Kommentare auf soz. Med.
Social media comments
61
Sachtexte, z.B. als
Blogposts...**
19
Prosa, Poesie
Prose, poetry
43
Witze
Jokes
24
Rezepte
Recipes
6
Notizen an mich selbst,
Tagebucheinträge***
63
Andere/weitere Sachen:
Other/additional things:
22
*Nachrichten in Chatprogrammen, Messengern (wie WhatsApp),
SMS Texts in messaging apps (like WhatsApp), text messages
**Sachtexte, z.B. als Blogposts oder auf Wikipedia Non-fiction
texts, e.g., blog posts or Wikipedia articles
***Notes to myself, diary entries
10. Setzen Sie sich aktiv für den Erhalt Ihres
Dialekts ein? (Mehrfachantworten möglich) Are
you actively involved in preserving your dialect?
(Multiple answers possible)
This question is based on questions in the sur-
veys by Soria et al. (2018) and Millour (2020,
pp. 227, 235). 323 respondents answered:
Ja, in einem Verein
zur Mundartpflege*
43
Ja, als Lehrer*in
Yes, as a teacher
14
Ja, anderweitig:
Yes, in another way:
73
Nein
No:
212
*Yes, in a dialect preservation society
11. Wie alt sind Sie? How old are you?
19 Jahre oder jünger
19 years or younger
7
20–29 Jahre
20–29 years
79
30–39 Jahre
30–39 years
78
40–49 Jahre
40–49 years
54
50–59 Jahre
50–59 years
39
60–69 Jahre
60–69 years
38
70–79 Jahre
70–79 years
25
80 Jahre oder älter
80 years or older
7
832
12. Weitere Kommentare zu Ihrem Dialekt oder
zu den vorherigen Fragen: (Optional) Additional
comments on your dialect or the preceding ques-
tions: (Optional)
61 write-in answers.
In diesem Abschnitt fragen wir Sie zu Ihrer Mei-
nung zu verschiedenen dialektbezogenen Themen.
Dabei gibt es keine richtigen/falschen oder er-
wünschten/unerwünschten Antworten, sondern
wir sind an Ihrer persönlichen Meinung interes-
siert. In this section we ask you about your opinion
on different dialect-related topics. There are no
right/wrong or desired/undesired answers, we are
simply interested in your personal opinion.
13. Stimmen Sie den folgenden Aussagen zu? Do
you agree with the following statements?
Statements presented in a randomized order:
• Die Vielfalt der unterschiedlichen Ausprägun-
gen meines Dialekts ist eine Stärke. The diver-
sity of the different varieties of my dialect is a
strength.
• Mein Dialekt ist in erster Linie eine gespro-
chene Sprachform und nicht für die schrift-
liche Kommunikation geeignet. My dialect
is primarily a spoken form of language and
not suited for written communication. This
question is based on an answer option in the
survey by Millour (2020, pp. 228, 237) (see
also question 8 in this appendix).
• Ich möchte meinen Dialekt in allen Lebensbe-
reichen verwenden. I’d like to be able to use
my dialect in any aspect of life. This question
is based on a question by Soria et al. (2018)
and Millour (2020, pp. 229, 239).
• Wenn ich einen Text lese, den jemand anderes
in meinem Dialekt verfasst hat, fällt es mir
schwer, ihn zu verstehen. When I read text
that someone else wrote in my dialect, I have
trouble understanding it.
• Es sollte eine standardisierte Rechtschreibung
für meinen Dialekt geben. There should be a
standardized orthography for my dialect.
Answer options:
• Ja, auf jeden Fall Yes, absolutely
• Eher ja Rather yes
• Weder noch Neither/nor
• Eher nein Rather no
• Nein, gar nicht Absolutely not
• Keine Angabe Prefer not to say
The answer distributions (in %) are as follows:
0
20
40
60
80
100 %
Orthography
Reading=hard
All aspects
Spoken only
Diversity
9
5
23
21
43
12
20
21
31
28
12
12
18
15
15
2
1
1
1
6
30
33
21
17
6
35
30
16
14
3
Disagree
N/A
A. somewhat
Dis. somewhat
Neither/nor
Agree
100
80
60
40
20
0 %
14. Weitere Kommentare zu diesem Abschnitt:
(Optional) Additional comments on this section:
(Optional)
48 write-in answers.
In diesem Abschnitt fragen wir Sie zu Ihrer Mei-
nung zu verschiedenen Sprachtechnologien. Dabei
gibt es keine richtigen/falschen oder erwünsch-
ten/unerwünschten Antworten, sondern wir sind
an Ihrer persönlichen Meinung interessiert. In this
section we ask you about your opinion on different
language technologies. There are no right/wrong
or desired/undesired answers, we are simply inter-
ested in your personal opinion.
Übersetzungsprogramme erstellen eine
automatische Übersetzung von Text aus ei-
ner Sprache in eine andere Sprache. Beispie-
le dafür sind DeepL oder Google Transla-
te. Machine translation software automati-
cally translate text from one language into
another. Examples are DeepL or Google
Translate.
833
15. Stimmen Sie den folgenden Aussagen zu?
Es sollte Übersetzungsprogramme geben, ... Do
you agree with the following statements? There
should be translation software...
• ...die hochdeutsche Texte in meinen Dialekt
übersetzen. ...that translates Standard Ger-
man texts into my dialect.
• ...die Texte aus anderen Sprachen in meinen
Dialekt übersetzen.
...that translates texts
from other languages into my dialect.
• ...die
Texte
aus
meinem
Dialekt
ins
Hochdeutsche übersetzen. ...that translates
texts from my dialect into Standard German.
• ...die Texte aus meinem Dialekt in andere
Sprachen übersetzen. ...that translates texts
from my dialect into other languages.
Answer options:
• Ja, unbedingt Yes, absolutely
• Eher ja Rather yes
• Weder noch Neither/nor
• Eher nein Rather no
• Nein, das halte ich nicht für sinnvoll No, I
don’t think this is useful
• Das kann ich nicht bewerten I cannot judge
this
See Figure 2 for answer distributions.
16. Welcher Aussage stimmen Sie mehr zu?
Wenn ich einen Text in meinen Dialekt über-
setzen lasse, ... With which statement do you agree
more? When a text is translated into my dialect, ...
1 – ...soll die Übersetzung
sprachlich meiner...*
52
2
61
3
67
4 32
5 – ...ist es mir egal,
welcher (geschriebenen)...**
43
Ich möchte keinen geschrie-
benen Dialekt-Output.***
72
*1 – ...soll die Übersetzung sprachlich meiner (geschriebenen)
Version des Dialekts voll und ganz entsprechen. 1 – ...the translation
should fully correspond to my own (written) version of the dialect.
**5 – ...ist es mir egal, welcher (geschriebenen) Form meines
Dialekts die Übersetzung sprachlich entspricht. 5 – ...I do not care
which (written) version of my dialect the translation corresponds to.
***I do not want any written dialect output.
17. Weitere Kommentare zu Übersetzungspro-
grammen: (Optional) Additional comments on
machine translation software: (Optional)
41 write-in answers.
Rechtschreib- und Grammatikkorrektur-
programme markieren oder korrigieren
mögliche Fehler in Texten, zum Beispiel
bei der Eingabe in Microsoft Word. Spell-
and grammar checkers highlight or fix po-
tential errors in texts, for instance when
writing text in Microsoft Word.
18. Stimmen Sie der folgenden Aussage zu? Es
sollte Rechtschreib- und Grammatikkorrektur-
programme für meinen Dialekt geben. Do you
agree with the following statement? There should
be spell- and grammar checkers for my dialect.
Same answer options as for question 15. See
Figure 2 for the answer distribution.
19. Weitere Kommentare zu Rechtschreib- und
Grammatikkorrekturprogrammen: (Optional)
Additional comments on spell- and grammar check-
ers: (Optional)
51 write-in answers.
Transkriptionsprogramme
verschrift-
lichen gesprochene Sprache. Sie finden
beispielsweise bei automatisch erzeugten
Untertiteln oder bei Diktiergeräten Einsatz.
Speech-to-text systems transcribe spoken
language. They are for instance used for
automatically generating subtitles or in the
context of dictation software.
20. Stimmen Sie den folgenden Aussagen zu? Es
sollte Transkriptionsprogramme geben, ... Do
you agree with the following statements? There
should be speech-to-text software...
• ...die Audioaufnahmen in meinem Dialekt
als geschriebenes Hochdeutsch wiedergeben.
...that transcribes audio recorded in my di-
alect as written Standard German.
• ...die Audioaufnahmen in meinem Dialekt als
geschriebenen Dialekt wiedergeben. ...that
transcribes audio recorded in my dialect as
written dialect.
834
Same answer options as for question 15. See Fig-
ure 2 for answer distributions.
21. Weitere Kommentare zu Transkriptionspro-
grammen: (Optional) Additional comments on
speech-to-text software: (Optional)
33 write-in answers.
Text-to-Speech-Systeme funktionieren um-
gekehrt wie Transkriptionsprogramme: sie
erzeugen gesprochene Versionen von ge-
schriebenem Text. Ein Beispiel dafür sind
Bildschirmleseprogramme. Text-to-speech
systems work the other way around as
speech-to-text systems: they generate spo-
ken versions of written text. One example
are screen readers.
22. Stimmen Sie der folgenden Aussage zu? Es
sollte Text-to-Speech-Systeme geben, die meinen
Dialekt von geschriebener Form in gesprochene
Form umwandeln. Do you agree with the fol-
lowing statement? There should be text-to-speech
systems that synthesize dialectal audio for text writ-
ten in my dialect.
Same answer options as for question 15. See
Figure 2 for the answer distribution.
23. Weitere Kommentare zu Text-to-Speech-
Systemen: (Optional) Additional comments on
text-to-speech systems: (Optional)
22 write-in answers.
Sprachassistenten sind Programme, die ge-
schriebene oder gesprochene Fragen beant-
worten bzw. Befehle ausführen, zum Bei-
spiel Siri oder Alexa.
Eng verwandt damit sind Chatbots: Pro-
gramme, die textbasierte Dialoge ermögli-
chen, bei denen ein Programm Antworten
auf Texteingaben von Nutzer*innen erzeugt.
Ein Beispiel dafür ist ChatGPT.
Digital assistants are programs that answer
written or spoken questions and carry out
commands, like Siri or Alexa.
Chatbots are closely related.
They are
software that enables text-based dialogues,
wherein a program generates answers to
text input from users. An example is Chat-
GPT.
24. Stimmen Sie den folgenden Aussagen zu?
Do you agree with the following statements?
• Es sollte Sprachassistenten geben, die man mit
Fragen/Befehlen in meinem Dialekt bedienen
kann. There should be digital assistants that
you can query with questions/commands in
my dialect.
• Es sollte Sprachassistenten geben, die in mei-
nem Dialekt auf Fragen/Befehle antworten.
There should be digital assistants that use my
dialect when replying to questions/commands.
• Es sollte Chatbots geben, die auf Eingaben
in meinem Dialekt antworten können. There
should be chatbots that can respond to inputs
written in my dialect.
• Es sollte Chatbots geben, deren Antworten in
meinem Dialekt verfasst sind. There should
be chatbots who respond in my dialect.
Same answer options as for question 15. See Fig-
ure 2 for answer the distributions.
25. Welcher Aussage stimmen Sie mehr zu?
Wenn ein Sprachassistent oder ein Chatbot Ant-
worten in meinem Dialekt erzeugt, ... With which
statement do you agree more? When a digital as-
sistant or chatbot generates replies in my dialect,
...
1 – ...sollen die Antworten
sprachlich meiner...*
46
2
69
3
61
4
28
5 – ...ist es mir egal, welcher
(geschriebenen oder...**
55
Ich möchte keinen geschrie-
benen Dialekt-Output.***
68
*1 – ...sollen die Antworten sprachlich meiner (geschriebenen oder
gesprochenen) Version des Dialekts voll und ganz entsprechen. 1 – ...
the replies should fully correspond to my own (written or spoken)
version of the dialect.
**5 – ...ist es mir egal, welcher (geschriebenen oder gesprochenen)
Form meines Dialekts die Antworten sprachlich entsprechen. 5 – ...I
do not care which (written or spoken) version of my dialect the replies
correspond to.
***I do not want any written dialect output.
26. Weitere Kommentare zu Sprachassistenten
oder Chatbots: (Optional) Additional comments
on digital assistants or chatbots: (Optional)
25 write-in answers.
835
Suchmaschinen sind Programme, die nach
einer Suchanfrage Datenbanken oder das In-
ternet nach relevanten Ergebnissen durchsu-
chen, wie zum Beispiel Google. Search en-
gines are programs that search a database
or the web based on a search query, like
Google.
27. Stimmen Sie der folgenden Aussage zu?
Es sollte Suchmaschinen geben, bei denen ich
meinen Dialekt als Eingabesprache verwenden
kann.
Do you agree with the following state-
ment? There should be search engines that support
queries in my dialect.
Same answer options as for question 15. See
Figure 2 for the answer distribution.
28. Weitere Kommentare zu Suchmaschinen:
(Optional) Additional comments on search engines:
(Optional)
15 write-in answers.
29. Sind Ihnen bereits Sprachtechnologien be-
kannt, die Ihren Dialekt unterstützen? Are you
already aware of any language technologies for
your dialect?
Ja und zwar:
Yes, namely:
46
Nein
No
280
30. Stimmen Sie den folgenden Aussagen zu?
Do you agree with the following statements?
Statements presented in a randomized order:
• Sprachtechnologie, die ich für sinnvoll hal-
te, nutze ich auch selbst. If I find language
technology useful, I also use it myself.
• Eine größere Unterstützung durch Sprachtech-
nologie würde meinen Dialekt attraktiver für
jüngere Generationen machen. If my dialect
were supported more by language technolo-
gies, the dialect would be more appealing for
younger generations. This question is mod-
elled after questions in the surveys by Soria
et al. (2018) and Millour (2020, p. 229), ask-
ing about the hypothesized impact of a lan-
guage’s increased use online on the appeal
for younger people.
• Wenn ich Sprachtechnologie für meinen Dia-
lekt hätte, würde ich ihn häufiger verwenden.
If I had language technology for my dialect, I
would use my dialect more often.
See question 13 for the answer options. Answer
distributions (in %):
0
20
40
60
80
100 %
More often
Appeal
Useful=use
11
20
25
21
33
38
15
14
12
4
10
6
28
15
14
22
8
6
Disagree
N/A
A. somewhat
Dis. somewhat
Neither/nor
Agree
100
80
60
40
20
0 %
31. Weitere Kommentare zum Thema Sprach-
technologie oder allgemein zu dieser Umfrage:
(Optional) Additional comments on language tech-
nology or generally regarding this survey: (Op-
tional)
29 write-in answers.
32. Wie haben Sie von dieser Studie erfahren?
How did you find out about this study?
Durch Forschende am
Centrum für ...*
41
Durch
Mundartpflegevereine**
20
Soziale Medien, Foren
Social media, forums
46
E-Mail-Verteiler
Mailing lists
119
(Anderweitig) durch
Bekannte***
79
Auf eine andere Art:
Otherwise:
20
*Durch Forschende am Centrum für Informations- und Sprachver-
arbeitung (LMU) Via researchers at the Center for Information and
Language Processing (LMU)
**Via dialect preservation societies
***(Otherwise) via acquaintances
Vielen Dank für Ihre Teilnahme! Wir möchten
uns ganz herzlich für Ihre Mithilfe bedanken. Ihre
Antworten wurden gespeichert, Sie können das
Browser-Fenster nun schließen. Thank you for
participating! We would like to thank you very
much for your help. Your answers have been saved;
you can close the browser window now.
836
All
Non-activists only
Rank
LTs
Mean
LTs
Mean
1
Assistant in (24)
3.75
Assistant in (24)
3.80
2
STT deu (20)
3.46
STT deu (20)
3.48
3
STT dial (20)
3.38
Chatbot in (24)
3.25
4
Chatbot in (24)
3.29
STT dial (20)
3.24
5
MT dial→deu (15)
3.17
Assistant out (24)
3.01
6
Assistant out (24)
3.14
MT dial→deu (15)
3.00
7
TTS (22)
3.13
TTS (22)
2.99
8
Search engines (27) 2.94
Search engines (27)
2.69
9
Chatbot out (24)
2.76
Chatbot out (24)
2.59
10
MT dial→oth (15)
2.73
MT dial→oth (15)
2.59
11
MT deu→dial (15)
2.71
MT deu→dial (15)
2.53
12
MT oth→dial (15)
2.39
MT oth→dial (15)
2.17
13
Spellcheckers (18)
2.38
Spellcheckers (18)
2.08
Table 1: Language technologies sorted by mean score given by all respondents and non-activists only (participants
who did not indicate involvement in language preservation, §4.4). ‘Mean’ refers to the mean Likert score (see text).
Numbers behind the LT names refer to questions in §A.
B
Correlation Scores
Figure 3 shows the Spearman’s rank correlation
coefficients (ρ) between the variables investigated
in the questionnaire, with ρ values ranging from
–0.50 to +0.77.
For the correlation analysis and the subgroup
comparisons (Appendix §D), the variable values
are mapped so that higher values correspond to
higher agreement with the statements in questions
13, 15, 18, 20, 22, 24, 27 and 30, and to higher
dialect competence (question 1) and usage fre-
quency (7), higher age (11) and age of dialect ac-
quisition (3), more traditional dialects (6),9 and
greater openness towards variation in the output of
MT (16) and digital assistants / chatbots (25). The
variable # writing contexts encodes the number of
answer options selected in question 9. The vari-
ables writing (8) and activism (10) are binary such
that 0 encodes the ‘no’ options and 1 stands for the
‘yes’ options.
The beginning of the first row in the figure can
thus be read as follows: Dialect competence self-
ratings are
• negatively correlated with the age of acquisi-
tion (i.e., respondents whose dialect is their
first language generally give higher compe-
tence ratings),
9Note that this the inverse of how the question is originally
phrased.
• slightly positively correlated with language ac-
tivism (i.e., fluent dialect speakers are slightly
more likely to be engaged in dialect preserva-
tion activities, and vice versa),
• positively correlated with traditionality (i.e.,
competent dialect speakers tend to rate their
dialect as more distinct from Standard Ger-
man, and vice versa),
and so on.
C
LT Ranking
Table 1 shows the order of preferred LTs. This
ranking is based on the mean scores when coding
the answers as follows: 1 = useless, 2 = rather use-
less, 3 = neither/not, 4 = rather useful, 5 = useful.
Non-answers (‘cannot judge’) are excluded.
If we remove the participants who indicated ac-
tive engagement in dialect preservation (see §4.4
and question 10), the ranking only changes very
slightly: chatbots with dialectal input and STT with
dialectal output trade places (although they have
nearly identical mean scores), and we observe the
same for virtual assistants with dialectal output and
machine translation from the dialect into Standard
German.
D
Subgroup Comparisons
Tables 2 and 3 show how the responses by different
subgroups of respondents differ for each variable.
837
We provide each subgroup’s mean answers (using
the same numeric coding as in the previous two
appendix sections), as well as t-test statistics (tak-
ing into account the scalar nature of the answer
options) and χ2 test results.
In addition to the analyses in §4.4, we provide
two more subgroup comparisons, albeit with small
effect sizes:
Traditionality
We compare the responses of
speakers who rate their dialect as traditional and
distinct from Standard German (the first two an-
swer options for question 6) to those who indicated
speaking a variety more akin to a regiolect (the last
two options). While these subgroups differ in their
responses to the dialect-related questions, few of
the differences regarding language technologies are
statistically significant (Table 2).
Age
Figure 3 shows that the variable age corre-
lates with few other variables. With respect to the
LTs, young participants tend to be somewhat more
positive towards three of the overall most popu-
lar LTs: STT with Standard German output, and
virtual assistants and chatbots with dialectal input.
838
Dialect skills (1)
Age of acquisition (3)
Age (11)
Activism (10)
Traditionality (6)
Frequency (7)
Writing (8)
# Writing contexts (9)
Any aspect (13)
Orthography (13)
Spoken only (13)
Diversity (13)
Reading is hard (13)
MT deu→dial (15)
MT oth→dial (15)
MT dial→deu (15)
MT dial→oth (15)
MT variation (16)
Spellcheckers (18)
STT deu (20)
STT dial (20)
TTS (22)
Assistant in (24)
Assistant out (24)
Chatbot in (24)
Chatbot out (24)
Assistant var. (25)
Search engines (27)
Know existing (29)
Appeal (30)
Useful=use (30)
More often (30)
Dialect skills (1)
Age of acquisition (3)
Age (11)
Activism (10)
Traditionality (6)
Frequency (7)
Writing (8)
# Writing contexts (9)
Any aspect (13)
Orthography (13)
Spoken only (13)
Diversity (13)
Reading is hard (13)
MT deu→dial (15)
MT oth→dial (15)
MT dial→deu (15)
MT dial→oth (15)
MT variation (16)
Spellcheckers (18)
STT deu (20)
STT dial (20)
TTS (22)
Assistant in (24)
Assistant out (24)
Chatbot in (24)
Chatbot out (24)
Assistant var. (25)
Search engines (27)
Know existing (29)
Appeal (30)
Useful=use (30)
More often (30)
0.75
0.50
0.25
0.00
–0.25
–0.50
Correlation (ρ)
<0.001
<0.01
<0.05
≥0.05
p-value
Figure 3: Spearman’s ρ between variables. Blue dots show positively
correlated variables (max.: +0.77), red dots negatively correlated ones (min.:
-0.50). We only include correlations with p-values under 0.05. The larger
the dot, the smaller the p-value. The numbers behind the variables refer to
the questions in Appendix §A. For further explanations of how the variables
are coded, see Appendix §B.
839
Variable
Activists vs. non-activists
Most vs. least trad. dialects
t-stat
χ2
µAct
µNon
t-stat
χ2
µMost
µLeast
Dialect skills (1)
3.0 **
9.0 *
3.7
3.5
11.3 ***
83.2 ***
3.8
2.9
Age of acquisition (3)
1.3
5.4
1.4
1.3
-4.4 ***
29.9 ***
1.2
1.5
Age (11)
6.7 ***
43.5 ***
4.7
3.4
0.7
3.9
3.9
3.7
Activism (10)
1.0
0.0
5.7 ***
26.9 ***
0.5
0.1
Traditionality (6)
5.6 ***
31.6 ***
3.9
3.2
4.3
1.6
Frequency (7)
2.5 *
14.0 *
5.1
4.6
5.0 ***
25.9 ***
5.1
4.0
Writing (8)
3.9 ***
13.9 ***
0.8
0.6
5.0 ***
21.3 ***
0.8
0.4
# Writing contexts (9)
4.9 ***
28.7 ***
3.3
2.2
3.4 ***
16.2 *
3.0
1.8
Any aspect (13)
5.6 ***
36.3 ***
3.7
2.8
6.2 ***
36.4 ***
3.5
2.2
Orthography (13)
6.0 ***
37.9 ***
2.8
2.0
2.8 **
12.6 *
2.5
1.9
Spoken only (13)
-4.2 ***
34.9 ***
2.9
3.5
-2.5 *
8.7
3.1
3.7
Diversity (13)
3.0 **
12.5 *
4.3
3.9
3.0 **
12.5 *
4.2
3.7
Reading is hard (13)
-1.6
5.5
2.2
2.4
-2.3 *
10.4 *
2.3
2.7
MT deu→dial (15)
3.0 **
14.7 **
3.0
2.5
0.9
2.0
2.7
2.5
MT oth→dial (15)
4.1 ***
21.8 ***
2.8
2.2
1.6
12.3 *
2.5
2.2
MT dial→deu (15)
3.1 **
13.1 *
3.5
3.0
1.5
7.9
3.2
2.9
MT dial→oth (15)
2.4 *
7.0
3.0
2.6
2.0 *
20.0 ***
2.9
2.4
MT variation (16)
-3.3 **
11.7 *
2.5
3.0
-4.2 ***
19.1 ***
2.6
3.6
Spellcheckers (18)
5.4 ***
34.2 ***
2.9
2.1
2.0 *
9.8 *
2.4
2.0
STT deu (20)
-0.3
3.1
3.4
3.5
1.0
8.9
3.5
3.3
STT dial (20)
2.4 *
8.7
3.6
3.2
2.6 **
10.1 *
3.5
2.9
TTS (22)
2.5 *
11.6 *
3.4
3.0
1.0
6.9
3.1
2.9
Assistant in (24)
-0.8
3.3
3.7
3.8
1.0
1.6
3.8
3.6
Assistant out (24)
2.3 *
5.6
3.4
3.0
-0.1
1.7
3.1
3.1
Chatbot in (24)
0.7
1.3
3.4
3.2
0.2
3.7
3.3
3.3
Chatbot out (24)
3.1 **
12.1 *
3.1
2.6
0.6
2.0
2.8
2.7
Assistant var. (25)
-3.5 ***
13.7 **
2.5
3.1
-4.9 ***
22.9 ***
2.7
3.7
Search engines (27)
4.2 ***
20.8 ***
3.4
2.7
0.9
4.2
2.9
2.7
Know existing (29)
0.6
0.2
0.2
0.1
2.0 *
3.1
0.2
0.1
Appeal (30)
5.4 ***
32.1 ***
4.0
3.2
1.5
8.0
3.6
3.3
Useful=use (30)
0.5
5.0
3.7
3.6
1.3
3.4
3.7
3.5
More often (30)
4.0 ***
16.5 **
3.1
2.5
0.1
1.0
2.6
2.6
Table 2: Differences between respondent subgroups. We show the results of t-tests and χ2 tests between pairs
of respondent groups: those who indicated involvement in dialect preservation efforts (‘activists’, question 10) vs.
those who did not, and respondents who rate their dialect as one of the two most vs. two least traditional options
(question 6). Positive t-statistics indicate that the first group’s values for the variable are higher than the second
one’s, and vice versa for negative values. Grey entries denote results with p-values ≥0.05; asterisks represent
smaller p-values: * < 0.05, ** < 0.01, *** < 0.001. The columns with µ present the mean Likert scores of the
subgroups’ responses (e.g., µAct contains the activists’ mean answers). The numbers behind the variables refer to
the questions in Appendix §A. For information on the variables on how the variables are encoded as numbers, see
Appendix §B.
840
NDS vs. (rest of) D/AT
CH vs. (non-NDS) D/AT
t-stat
χ2
t-stat
χ2
µNDS
µD/AT
µCH
Dialect skills (1)
-1.6
5.5
4.7 ***
23.4 ***
3.4
3.5
4.0
Age of acquisition (3)
5.2 ***
25.2 ***
-3.2 **
10.0 **
1.7
1.3
1.1
Age (11)
6.3 ***
38.2 ***
-1.5
15.1 *
5.3
3.6
3.2
Activism (10)
5.3 ***
23.9 ***
-1.8
2.5
0.7
0.3
0.2
Traditionality (6)
2.8 **
9.9 *
3.3 **
16.6 **
3.7
3.3
3.8
Frequency (7)
-1.1
6.4
4.6 ***
23.0 ***
4.4
4.7
5.7
Writing (8)
1.2
1.1
3.8 ***
12.5 ***
0.7
0.6
0.9
# Writing contexts (9)
6.5 ***
45.9 ***
4.5 ***
25.5 ***
4.0
2.1
3.2
Any aspect (13)
5.3 ***
27.6 ***
3.9 ***
27.3 ***
3.9
2.8
3.7
Orthography (13)
8.4 ***
63.6 ***
-2.0 *
4.8
3.6
2.0
1.7
Spoken only (13)
-5.6 ***
30.6 ***
-3.0 **
19.7 ***
2.5
3.6
3.0
Diversity (13)
1.0
3.1
2.7 **
7.3
4.2
4.0
4.5
Reading is hard (13)
-4.1 ***
18.3 **
-1.8
3.2
1.8
2.6
2.2
MT deu→dial (15)
4.6 ***
21.3 ***
-0.5
1.6
3.5
2.5
2.4
MT oth→dial (15)
5.0 ***
27.8 ***
-0.2
5.0
3.2
2.2
2.2
MT dial→deu (15)
2.6 **
9.9 *
1.0
4.3
3.6
3.0
3.3
MT dial→oth (15)
3.3 **
13.6 **
1.3
4.4
3.2
2.5
2.8
MT variation (16)
-2.4 *
11.6 *
0.7
4.5
2.3
2.9
3.0
Spellcheckers (18)
8.2 ***
68.3 ***
-2.1 *
8.2
3.7
2.1
1.7
STT deu (20)
-1.5
9.5
3.2 **
10.5 *
3.1
3.4
4.1
STT dial (20)
4.0 ***
17.5 **
2.6 **
10.7 *
4.0
3.1
3.7
TTS (22)
4.0 ***
15.9 **
0.5
1.5
3.8
2.9
3.1
Assistant in (24)
-0.8
7.6
2.7 **
9.1
3.5
3.7
4.2
Assistant out (24)
1.7
3.0
0.8
2.6
3.4
3.0
3.2
Chatbot in (24)
0.8
2.0
-0.6
2.1
3.4
3.2
3.1
Chatbot out (24)
3.7 ***
13.4 **
-0.4
2.8
3.4
2.6
2.5
Assistant var. (25)
-2.3 *
9.2
1.1
2.1
2.4
2.9
3.2
Search engines (27)
4.8 ***
26.1 ***
-1.6
3.8
3.8
2.8
2.4
Know existing (29)
4.1 ***
13.6 ***
8.2 ***
49.5 ***
0.2
0.1
0.5
Appeal (30)
5.1 ***
35.3 ***
-3.1 **
14.0 **
4.3
3.3
2.7
Useful=use (30)
0.1
1.4
1.6
3.8
3.6
3.6
3.9
More often (30)
3.7 ***
14.8 **
-1.6
3.1
3.2
2.5
2.2
Table 3: Differences between region-based respondent subgroups. We show the results of t-tests and χ2 tests
between Low German (NDS) or Swiss (CH) respondents compared to (non-Low-German-speaking) German and
Austrian respondents (D/AT). Positive t-statistics indicate that the first group’s values for the variable are higher
than the second one’s, and vice versa for negative values. Grey entries denote results with p-values ≥0.05; asterisks
represent smaller p-values: * < 0.05, ** < 0.01, *** < 0.001. The columns with µ present the mean Likert scores
of the subgroups’ responses (e.g., µNDS contains the mean answers provided by our Low Saxon respondents). The
numbers behind the variables refer to the questions in Appendix §A. For information on the variables on how the
variables are encoded as numbers, see Appendix §B.
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