Hauptthemen unserer Publikationen zu Schweizerdeutsch:
2023
Proceedings Articles
Plüss, Michel; Deriu, Jan; Schraner, Yanick; Paonessa, Claudio; Hartmann, Julia; Schmidt, Larissa; Scheller, Christian; Hürlimann, Manuela; Samardžić, Tanja; Vogel, Manfred; Cieliebak, Mark
STT4SG-350: A Speech Corpus for All Swiss German Dialect Regions Proceedings Article
In: 61st Annual Meeting of the Association for Computational Linguistics (ACL), (Hrsg.): S. 1763–1772, 2023.
Abstract | Links | BibTeX | Schlagwörter: Corpus
@inproceedings{nokey,
title = {STT4SG-350: A Speech Corpus for All Swiss German Dialect Regions},
author = {Michel Plüss and Jan Deriu and Yanick Schraner and Claudio Paonessa and Julia Hartmann and Larissa Schmidt and Christian Scheller and Manuela Hürlimann and Tanja Samardžić and Manfred Vogel and Mark Cieliebak},
editor = {61st Annual Meeting of the Association for Computational Linguistics (ACL)},
url = {https://aclanthology.org/2023.acl-short.150/},
doi = {10.18653/v1/2023.acl-short.150},
year = {2023},
date = {2023-05-01},
urldate = {2023-05-01},
pages = {1763–1772},
abstract = {We present STT4SG-350, a corpus of Swiss German speech, annotated with Standard German text at the sentence level. The data is collected using a web app in which the speakers are shown Standard German sentences, which they translate to Swiss German and record. We make the corpus publicly available. It contains 343 hours of speech from all dialect regions and is the largest public speech corpus for Swiss German to date. Application areas include automatic speech recognition (ASR), text-to-speech, dialect identification, and speaker recognition. Dialect information, age group, and gender of the 316 speakers are provided. Genders are equally represented and the corpus includes speakers of all ages. Roughly the same amount of speech is provided per dialect region, which makes the corpus ideally suited for experiments with speech technology for different dialects. We provide training, validation, and test splits of the data. The test set consists of the same spoken sentences for each dialect region and allows a fair evaluation of the quality of speech technologies in different dialects. We train an ASR model on the training set and achieve an average BLEU score of 74.7 on the test set. The model beats the best published BLEU scores on 2 other Swiss German ASR test sets, demonstrating the quality of the corpus.},
keywords = {Corpus},
pubstate = {published},
tppubtype = {inproceedings}
}
We present STT4SG-350, a corpus of Swiss German speech, annotated with Standard German text at the sentence level. The data is collected using a web app in which the speakers are shown Standard German sentences, which they translate to Swiss German and record. We make the corpus publicly available. It contains 343 hours of speech from all dialect regions and is the largest public speech corpus for Swiss German to date. Application areas include automatic speech recognition (ASR), text-to-speech, dialect identification, and speaker recognition. Dialect information, age group, and gender of the 316 speakers are provided. Genders are equally represented and the corpus includes speakers of all ages. Roughly the same amount of speech is provided per dialect region, which makes the corpus ideally suited for experiments with speech technology for different dialects. We provide training, validation, and test splits of the data. The test set consists of the same spoken sentences for each dialect region and allows a fair evaluation of the quality of speech technologies in different dialects. We train an ASR model on the training set and achieve an average BLEU score of 74.7 on the test set. The model beats the best published BLEU scores on 2 other Swiss German ASR test sets, demonstrating the quality of the corpus.
2022
Proceedings Articles
Plüss, Michel; Hürlimann, Manuela; Cuny, Marc; Stockli, Alla; Kapotis, Nikolaos; Hartmann, Julia; Ulasik, Malgorzata Anna; Scheller, Christian; Schraner, Yanick; Jain, Amit; Deriu, Jan; Cieliebak, Mark; Vogel, Manfred
SDS-200: A Swiss German Speech to Standard German Text Corpus Proceedings Article
In: 13th International Conference on Language Resources,; (LREC), Evaluation (Hrsg.): 2022.
Links | BibTeX | Schlagwörter: Corpus, Endangered Languages, Less-Resourced Languages, Speech Recognition/Understanding, Statistical and Machine Learning Methods
@inproceedings{,
title = {SDS-200: A Swiss German Speech to Standard German Text Corpus},
author = {Michel Plüss and Manuela Hürlimann and Marc Cuny and Alla Stockli and Nikolaos Kapotis and Julia Hartmann and Malgorzata Anna Ulasik and Christian Scheller and Yanick Schraner and Amit Jain and Jan Deriu and Mark Cieliebak and Manfred Vogel},
editor = {13th International Conference on Language Resources and Evaluation (LREC)},
url = {http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.347.pdf
https://doi.org/10.48550/arXiv.2205.09501},
doi = {10.48550/arXiv.2205.09501},
year = {2022},
date = {2022-05-19},
urldate = {2022-05-19},
keywords = {Corpus, Endangered Languages, Less-Resourced Languages, Speech Recognition/Understanding, Statistical and Machine Learning Methods},
pubstate = {published},
tppubtype = {inproceedings}
}
Forschungsberichte
Schraner, Yanick; Scheller, Christian; Plüss, Michel; Vogel, Manfred
Swiss German Speech to Text Evaluation Forschungsbericht
2022.
Abstract | Links | BibTeX | Schlagwörter: speech translation, Speech-to-Text, Swiss German, System Evaluation
@techreport{nokey,
title = {Swiss German Speech to Text Evaluation},
author = {Yanick Schraner and Christian Scheller and Michel Plüss and Manfred Vogel
},
editor = {University of Applied Sciences and Arts Northwestern Switzerland},
url = {https://arxiv.org/pdf/2207.00412.pdf},
year = {2022},
date = {2022-11-14},
urldate = {2022-11-14},
abstract = {We present an in-depth evaluation of four commercially available Speech-to-Text (STT) systems
for Swiss German. The systems are anonymized and referred to as system a, b, c and d in this
report. We compare the four systems to our STT models, referred to as FHNW in the following,
and provide details on how we trained our model. To evaluate the models, we use two STT datasets
from different domains. The Swiss Parliament Corpus (SPC) test set and the STT4SG-350 corpus,
which contains texts from the news sector with an even distribution across seven dialect regions. We
provide a detailed error analysis to detect the strengths and weaknesses of the different systems. On
both datasets, our model achieves the best results for both, the WER (word error rate) and the BLEU
(bilingual evaluation understudy) scores. On the SPC test set, we obtain a BLEU score of 0.607,
whereas the best commercial system reaches a BLEU score of 0.509. On the STT4SG-350 test set,
we obtain a BLEU score of 0.722, while the best commercial system achieves a BLEU score of 0.568.
However, we would like to point out that this analysis is somewhat limited by the domain-specific
idiosyncrasies of the selected texts of the two test sets.
},
keywords = {speech translation, Speech-to-Text, Swiss German, System Evaluation},
pubstate = {published},
tppubtype = {techreport}
}
We present an in-depth evaluation of four commercially available Speech-to-Text (STT) systems
for Swiss German. The systems are anonymized and referred to as system a, b, c and d in this
report. We compare the four systems to our STT models, referred to as FHNW in the following,
and provide details on how we trained our model. To evaluate the models, we use two STT datasets
from different domains. The Swiss Parliament Corpus (SPC) test set and the STT4SG-350 corpus,
which contains texts from the news sector with an even distribution across seven dialect regions. We
provide a detailed error analysis to detect the strengths and weaknesses of the different systems. On
both datasets, our model achieves the best results for both, the WER (word error rate) and the BLEU
(bilingual evaluation understudy) scores. On the SPC test set, we obtain a BLEU score of 0.607,
whereas the best commercial system reaches a BLEU score of 0.509. On the STT4SG-350 test set,
we obtain a BLEU score of 0.722, while the best commercial system achieves a BLEU score of 0.568.
However, we would like to point out that this analysis is somewhat limited by the domain-specific
idiosyncrasies of the selected texts of the two test sets.
for Swiss German. The systems are anonymized and referred to as system a, b, c and d in this
report. We compare the four systems to our STT models, referred to as FHNW in the following,
and provide details on how we trained our model. To evaluate the models, we use two STT datasets
from different domains. The Swiss Parliament Corpus (SPC) test set and the STT4SG-350 corpus,
which contains texts from the news sector with an even distribution across seven dialect regions. We
provide a detailed error analysis to detect the strengths and weaknesses of the different systems. On
both datasets, our model achieves the best results for both, the WER (word error rate) and the BLEU
(bilingual evaluation understudy) scores. On the SPC test set, we obtain a BLEU score of 0.607,
whereas the best commercial system reaches a BLEU score of 0.509. On the STT4SG-350 test set,
we obtain a BLEU score of 0.722, while the best commercial system achieves a BLEU score of 0.568.
However, we would like to point out that this analysis is somewhat limited by the domain-specific
idiosyncrasies of the selected texts of the two test sets.
Schraner, Yanick; Scheller, Christian; Plüss, Michel; Neukom, Lukas; Vogel, Manfred
Comparison of Unsupervised Learning and Supervised Learning with Noisy Labels for Low-Resource Speech Recognition Forschungsbericht
2022.
Links | BibTeX | Schlagwörter: forced-alignment, low-resource, self-supervised, semi-supervised, Speech Recognition/Understanding, speech translation
@techreport{nokey,
title = {Comparison of Unsupervised Learning and Supervised Learning with Noisy Labels for Low-Resource Speech Recognition},
author = {Yanick Schraner and Christian Scheller and Michel Plüss and Lukas Neukom and Manfred Vogel},
editor = {University of Applied Sciences and Arts Northwestern Switzerland},
url = {https://www.isca-speech.org/archive/pdfs/interspeech_2022/schraner22_interspeech.pdf},
year = {2022},
date = {2022-09-22},
keywords = {forced-alignment, low-resource, self-supervised, semi-supervised, Speech Recognition/Understanding, speech translation},
pubstate = {published},
tppubtype = {techreport}
}
2021
Dokumentationen
Ulasik, Malgorzata Anna; Hürlimann, Manuela; Dubel, Bogumila; Kaufmann, Yves; Rudolf, Silas; Deriu, Jan; Mlynchyk, Katsiaryna; Hutter, Hans-Peter; Cieliebak, Mark
ZHAW-CAI : Ensemble Method for Swiss German Speech to Standard German Text Dokumentation
on Swiss German Speech to Standard German Text Shared Task at 6th Swiss Text Analytics Conference (SwissText), Shared Task (Hrsg.): 2021, ISSN: 1613-0073.
Abstract | Links | BibTeX | Schlagwörter: speech translation
@manual{nokey,
title = {ZHAW-CAI : Ensemble Method for Swiss German Speech to Standard German Text},
author = {Malgorzata Anna Ulasik and Manuela Hürlimann and Bogumila Dubel and Yves Kaufmann and Silas Rudolf and Jan Deriu and Katsiaryna Mlynchyk and Hans-Peter Hutter and Mark Cieliebak},
editor = {Shared Task on Swiss German Speech to Standard German Text Shared Task at 6th Swiss Text Analytics Conference (SwissText)},
url = {http://ceur-ws.org/Vol-2957/sg_paper3.pdf
https://digitalcollection.zhaw.ch/handle/11475/23889
https://doi.org/10.21256/zhaw-23889},
doi = {10.21256/zhaw-23889},
issn = {1613-0073},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
abstract = {This paper presents the contribution of ZHAW-CAI to the Shared Task ”Swiss German Speech to Standard German Text” at the SwissText 2021 conference. Our approach combines three models based on the Fairseq, Jasper and Wav2vec architectures trained on multilingual, German and Swiss German data. We applied an ensembling algorithm on the predictions of the three models in order to retrieve the most reliable candidate out of the provided translations for each spoken utterance. With the ensembling output, we achieved a BLEU score of 39.39 on the private test set, which gave us the third place out of four contributors in the competition.},
keywords = {speech translation},
pubstate = {published},
tppubtype = {manual}
}
This paper presents the contribution of ZHAW-CAI to the Shared Task ”Swiss German Speech to Standard German Text” at the SwissText 2021 conference. Our approach combines three models based on the Fairseq, Jasper and Wav2vec architectures trained on multilingual, German and Swiss German data. We applied an ensembling algorithm on the predictions of the three models in order to retrieve the most reliable candidate out of the provided translations for each spoken utterance. With the ensembling output, we achieved a BLEU score of 39.39 on the private test set, which gave us the third place out of four contributors in the competition.
Forschungsberichte
Plüss, Michel; Neukom, Lukas; Vogel, Manfred
SwissText 2021 Task 3: Swiss German Speech to Standard German Text Forschungsbericht
2021.
Abstract | Links | BibTeX | Schlagwörter: speech translation, Speech-to-Text
@techreport{nokey,
title = {SwissText 2021 Task 3: Swiss German Speech to Standard German Text},
author = {Michel Plüss and Lukas Neukom and Manfred Vogel },
editor = {Institute for Data Science
University of Applied Sciences and Arts Northwestern Switzerland
Windisch, Switzerland
},
url = {https://ceur-ws.org/Vol-2957/sg_paper1.pdf},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
abstract = {We present the results and findings of SwissText 2021 Task 3 on Swiss German Speech to Standard German Text. Participants were asked to build a system translating Swiss German speech to Standard German text. The objective was to maximize the BLEU score on a new test set covering a large part of the Swiss German dialect landscape. Four teams participated, with the winning contribution achieving a BLEU score of 46.0.},
keywords = {speech translation, Speech-to-Text},
pubstate = {published},
tppubtype = {techreport}
}
We present the results and findings of SwissText 2021 Task 3 on Swiss German Speech to Standard German Text. Participants were asked to build a system translating Swiss German speech to Standard German text. The objective was to maximize the BLEU score on a new test set covering a large part of the Swiss German dialect landscape. Four teams participated, with the winning contribution achieving a BLEU score of 46.0.
2020
Proceedings Articles
Benites, Fernando; Hürlimann, Manuela; von Däniken, Pius; Cieliebak, Mark
ZHAW-InIT – Social Media Geolocation at VarDial 2020 Proceedings Article
In: on Computational Linguistics (ICCL), International Committee (Hrsg.): S. 254–264, International Committee on Computational Linguistics (ICCL), Barcelona, Spain (Online), 2020.
Abstract | Links | BibTeX | Schlagwörter: Endangered Languages, Geolocation, Less-Resourced Languages, Speech Recognition/Understanding
@inproceedings{nokey,
title = {ZHAW-InIT – Social Media Geolocation at VarDial 2020},
author = {Fernando Benites and Manuela Hürlimann and Pius von Däniken and Mark Cieliebak},
editor = {International Committee on Computational Linguistics (ICCL)},
url = {https://aclanthology.org/2020.vardial-1.24
https://aclanthology.org/2020.vardial-1.24.pdf},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
pages = {254–264},
publisher = {International Committee on Computational Linguistics (ICCL)},
address = {Barcelona, Spain (Online)},
abstract = {We describe our approaches for the Social Media Geolocation (SMG) task at the VarDial Evaluation Campaign 2020. The goal was to predict geographical location (latitudes and longitudes) given an input text. There were three subtasks corresponding to German-speaking Switzerland (CH), Germany and Austria (DE-AT), and Croatia, Bosnia and Herzegovina, Montenegro and Serbia (BCMS). We submitted solutions to all subtasks but focused our development efforts on the CH subtask, where we achieved third place out of 16 submissions with a median distance of 15.93 km and had the best result of 14 unconstrained systems. In the DE-AT subtask, we ranked sixth out of ten submissions (fourth of 8 unconstrained systems) and for BCMS we achieved fourth place out of 13 submissions (second of 11 unconstrained systems).},
keywords = {Endangered Languages, Geolocation, Less-Resourced Languages, Speech Recognition/Understanding},
pubstate = {published},
tppubtype = {inproceedings}
}
We describe our approaches for the Social Media Geolocation (SMG) task at the VarDial Evaluation Campaign 2020. The goal was to predict geographical location (latitudes and longitudes) given an input text. There were three subtasks corresponding to German-speaking Switzerland (CH), Germany and Austria (DE-AT), and Croatia, Bosnia and Herzegovina, Montenegro and Serbia (BCMS). We submitted solutions to all subtasks but focused our development efforts on the CH subtask, where we achieved third place out of 16 submissions with a median distance of 15.93 km and had the best result of 14 unconstrained systems. In the DE-AT subtask, we ranked sixth out of ten submissions (fourth of 8 unconstrained systems) and for BCMS we achieved fourth place out of 13 submissions (second of 11 unconstrained systems).
Dokumentationen
Büchi, Matthias; Ulasik, Malgorzata Anna; Hürlimann, Manuela; Benites, Fernando; von Däniken, Pius; Cieliebak, Mark
ZHAW-InIT at GermEval 2020 Task 4: Low-Resource Speech-to-Text Dokumentation
at GermEval, Low-Resource Speech-to-Text Shared Task (Hrsg.): 2020, ISSN: 1613-0073.
Abstract | Links | BibTeX | Schlagwörter: CNN, low-resource, speech translation, Speech-to-Text
@manual{nokey,
title = {ZHAW-InIT at GermEval 2020 Task 4: Low-Resource Speech-to-Text },
author = {Matthias Büchi and Malgorzata Anna Ulasik and Manuela Hürlimann and Fernando Benites and Pius von Däniken and Mark Cieliebak},
editor = {Low-Resource Speech-to-Text Shared Task at GermEval},
url = {https://doi.org/10.21256/zhaw-21550
https://digitalcollection.zhaw.ch/handle/11475/21550},
doi = {10.21256/zhaw-21550},
issn = {1613-0073},
year = {2020},
date = {2020-06-01},
urldate = {2020-06-01},
abstract = {This paper presents the contribution of ZHAW-InIT to Task 4 ”Low-Resource STT” at GermEval 2020. The goal of the task is to develop a system for translating Swiss German dialect speech into Standard German text in the domain of parliamentary debates. Our approach is based on Jasper, a CNN Acoustic Model, which we fine-tune on the task data. We enhance the base system with an extended Language Model containing in-domain data and speed perturbation and run further experiments with post-processing. Our submission achieved first place with a final Word Error Rate of 40.29%.},
keywords = {CNN, low-resource, speech translation, Speech-to-Text},
pubstate = {published},
tppubtype = {manual}
}
This paper presents the contribution of ZHAW-InIT to Task 4 ”Low-Resource STT” at GermEval 2020. The goal of the task is to develop a system for translating Swiss German dialect speech into Standard German text in the domain of parliamentary debates. Our approach is based on Jasper, a CNN Acoustic Model, which we fine-tune on the task data. We enhance the base system with an extended Language Model containing in-domain data and speed perturbation and run further experiments with post-processing. Our submission achieved first place with a final Word Error Rate of 40.29%.
Forschungsberichte
Plüss, Michel; Neukom, Lukas; Scheller, Christian; Vogel, Manfred
Swiss Parliaments Corpus, an Automatically Aligned Swiss German Speech to Standard German Text Corpus Forschungsbericht
2020.
Abstract | Links | BibTeX | Schlagwörter: Corpus, forced-alignment
@techreport{nokey,
title = {Swiss Parliaments Corpus, an Automatically Aligned Swiss German Speech to Standard German Text Corpus},
author = {Michel Plüss and Lukas Neukom and Christian Scheller and Manfred Vogel },
editor = {Institute for Data Science
University of Applied Sciences and Arts Northwestern Switzerland
Windisch, Switzerland},
url = {https://ceur-ws.org/Vol-2957/paper3.pdf},
year = {2020},
date = {2020-10-06},
urldate = {2020-10-06},
abstract = {We present the Swiss Parliaments Corpus (SPC), an automatically aligned Swiss German speech to Standard German text corpus. This first version of the corpus is based on publicly available data of the Bernese cantonal parliament and consists of 293 hours of data. It was created using a novel forced sentence alignment procedure and an alignment quality estimator, which can be used to trade off corpus size and quality. We trained Automatic Speech Recognition (ASR) models as baselines on different subsets of the data and achieved a Word Error Rate (WER) of 0.278 and a BLEU score of 0.586 on the SPC test set. The corpus is freely available for download.},
keywords = {Corpus, forced-alignment},
pubstate = {published},
tppubtype = {techreport}
}
We present the Swiss Parliaments Corpus (SPC), an automatically aligned Swiss German speech to Standard German text corpus. This first version of the corpus is based on publicly available data of the Bernese cantonal parliament and consists of 293 hours of data. It was created using a novel forced sentence alignment procedure and an alignment quality estimator, which can be used to trade off corpus size and quality. We trained Automatic Speech Recognition (ASR) models as baselines on different subsets of the data and achieved a Word Error Rate (WER) of 0.278 and a BLEU score of 0.586 on the SPC test set. The corpus is freely available for download.
Plüss, Michel; Neukom, Lukas; Vogel, Manfred
GermEval 2020 Task 4: Low-Resource Speech-to-Text Forschungsbericht
2020.
Abstract | Links | BibTeX | Schlagwörter: low-resource, speech translation, Speech-to-Text
@techreport{nokey,
title = {GermEval 2020 Task 4: Low-Resource Speech-to-Text},
author = {Michel Plüss and Lukas Neukom and Manfred Vogel},
editor = {Institute for Data Science
University of Applied Sciences and Arts Northwestern Switzerland
Windisch, Switzerland},
url = {https://ceur-ws.org/Vol-2624/germeval-task4-paper1.pdf},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
abstract = {We present the results and findings of GermEval 2020 Task 4 on Low-Resource Speech-to-Text. Participants were asked to build a system translating Swiss German speech to Standard German text and minimize its word error rate. The task was based on a new dataset for Swiss German to Standard German speech translation, which contains 74 hours of sentence-level speech-text-pairs. 3 teams participated, with the winning contribution reaching a word error rate of 40.29 %.
},
keywords = {low-resource, speech translation, Speech-to-Text},
pubstate = {published},
tppubtype = {techreport}
}
We present the results and findings of GermEval 2020 Task 4 on Low-Resource Speech-to-Text. Participants were asked to build a system translating Swiss German speech to Standard German text and minimize its word error rate. The task was based on a new dataset for Swiss German to Standard German speech translation, which contains 74 hours of sentence-level speech-text-pairs. 3 teams participated, with the winning contribution reaching a word error rate of 40.29 %.
von Däniken, Pius; Hürlimann, Manuela; Cieliebak, Mark
Overview of the GermEval 2020 Shared Task on Swiss German Language Identification Forschungsbericht
2020.
Abstract | Links | BibTeX | Schlagwörter: Endangered Languages, Less-Resourced Languages
@techreport{nokey,
title = {Overview of the GermEval 2020 Shared Task on Swiss German Language Identification},
author = {Pius von Däniken and Manuela Hürlimann and Mark Cieliebak},
editor = {Institute of Applied Information Technology
Zurich University of Applied Sciences},
url = {https://ceur-ws.org/Vol-2624/germeval-task2-paper1.pdf},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
abstract = {In this paper, we present the findings of the Shared Task on Swiss German Language Identification organised as part of the 7th edition of GermEval, co-located with SwissText and KONVENS 2020.},
keywords = {Endangered Languages, Less-Resourced Languages},
pubstate = {published},
tppubtype = {techreport}
}
In this paper, we present the findings of the Shared Task on Swiss German Language Identification organised as part of the 7th edition of GermEval, co-located with SwissText and KONVENS 2020.
2018
Proceedings Articles
Grubenmann, Ralf; Tuggener, Don; von Däniken, Pius; Deriu, Jan; Cieliebak, Mark
SB-CH: A Swiss German Corpus with Sentiment Annotations Proceedings Article
In: 11th International Conference on Language Resources,; (LREC), Evaluation (Hrsg.): 2018, ISBN: 979-10-95546-00-9.
Links | BibTeX | Schlagwörter: Corpus, Endangered Languages, Less-Resourced Languages, sentiment annotation
@inproceedings{nokey,
title = {SB-CH: A Swiss German Corpus with Sentiment Annotations},
author = {Ralf Grubenmann and Don Tuggener and Pius von Däniken and Jan Deriu and Mark Cieliebak},
editor = {11th International Conference on Language Resources and Evaluation (LREC)},
url = {https://digitalcollection.zhaw.ch/handle/11475/10819},
isbn = {979-10-95546-00-9},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
keywords = {Corpus, Endangered Languages, Less-Resourced Languages, sentiment annotation},
pubstate = {published},
tppubtype = {inproceedings}
}
Forschungsberichte
Samardžić, Tanja; Cieliebak, Mark; Deriu, Jan
Future Actions for Swiss German — Workshop Results at SwissText 2018 Forschungsbericht
2018.
Abstract | Links | BibTeX | Schlagwörter: Endangered Languages, Less-Resourced Languages
@techreport{nokey,
title = {Future Actions for Swiss German — Workshop Results at SwissText 2018},
author = {Tanja Samardžić and Mark Cieliebak and Jan Deriu},
url = {https://ceur-ws.org/Vol-2226/paper12.pdf},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
abstract = {The goal of this workshop was to initiate collaborations among companies and academic institutions for developing Swiss German resources and activities. The need for such an initiative is created by a growing interest for applying automatic text processing technologies to Swiss German, which takes place in the context of particularly scarce data sets. We have considered potential modes for a collaborative data development and management. The outcome of the workshop are defined common interests, priorities, and the first steps in future synchronised efforts.},
keywords = {Endangered Languages, Less-Resourced Languages},
pubstate = {published},
tppubtype = {techreport}
}
The goal of this workshop was to initiate collaborations among companies and academic institutions for developing Swiss German resources and activities. The need for such an initiative is created by a growing interest for applying automatic text processing technologies to Swiss German, which takes place in the context of particularly scarce data sets. We have considered potential modes for a collaborative data development and management. The outcome of the workshop are defined common interests, priorities, and the first steps in future synchronised efforts.