Meenzerisch, the dialect spoken in the German city of Mainz, is also the traditional language of the Mainz carnival, a yearly celebration well known throughout Germany. However, Meenzerisch is on the verge of dying out-a fate it shares with many other German dialects. Natural language processing (NLP) has the potential to help with the preservation and revival efforts of languages and dialects. However, so far no NLP research has looked at Meenzerisch. This work presents the first research in the field of NLP that is explicitly focused on the dialect of Mainz. We introduce a digital dictionary-an NLP-ready dataset derived from an existing resource (Schramm, 1966)-to support researchers in modeling and benchmarking the language. It contains 2,351 words in the dialect paired with their meanings described in Standard German. We then use this dataset to answer the following research questions: (1) Can state-of-the-art large language models (LLMs) generate definitions for dialect words? (2) Can LLMs generate words in Meenzerisch, given their definitions? Our experiments show that LLMs can do neither: the best model for definitions reaches only 6.27% accuracy and the best word generation model's accuracy is 1.51%. We then conduct two additional experiments in order to see if accuracy is improved by few-shot learning and by extracting rules from the training set, which are then passed to the LLM. While those approaches are able to improve the results, accuracy remains below 10%. This highlights that additional resources and an intensification of research efforts focused on German dialects are desperately needed.
Large Language Models Discriminate Against Speakers of German Dialects
Johannes Gutenberg University Mainz（明斯特-贾格施泰因大学）；Data Science Group, University of Hamburg（汉堡大学数据科学组）；Allen Institute for AI（人工智能研究所）；University of Washington（华盛顿大学）；University of Colorado Boulder（科罗拉多大学博尔德分校）
Assessing Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks
University of Oxford（牛津大学）；Microsoft Research（微软研究院）；The Alan Turing Institute（艾伦·图灵研究所）；Allen Institute for AI（人工智能研究所）；University of Washington（华盛顿大学）；Microsoft Corporation（微软公司）；University of York（约克大学）
LLMs Silently Correct African American English: Auditing and Mitigating Dialect Bias via Activation Steering
York University（约克大学）；Vector Institute（向量研究所）；Connected Minds（连接思维公司）；Emory University（埃默里大学）；Wilfrid Laurier University（威尔弗里德·劳里埃大学）；University of Toronto（多伦多大学）；University of Guelph（圭尔夫大学）；Monark Health（莫纳克健康公司）；CIFAR Solution Network（加拿大高级研究院解决方案网络）
What Do Dialect Speakers Want? A Survey of Attitudes Towards Language Technology for German Dialects
Center for Information and Language Processing (CIS), LMU Munich（慕尼黑大学信息与语言处理中心）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Department of Humanities, University of Luxembourg（卢森堡大学人文系）；Department of Computer Science, IT University of Copenhagen（哥本哈根信息技术大学计算机科学系）
Native Design Bias: Studying the Impact of English Nativeness on Language Model Performance
Research Centre for Information Systems Engineering (LIRIS), KU Leuven（信息系统工程研究中心（LIRIS），库勒文大学）；IESEG School of Management（IESEG管理学院）；Department of Decision Analytics and Risk, University of Southampton（决策分析与风险系，南安普顿大学）
STT4SG-350: A Speech Corpus for All Swiss German Dialect Regions
University of Applied Sciences and Arts Northwestern Switzerland（瑞士西北应用科学与艺术大学）；Zurich University of Applied Sciences（苏黎世应用科学大学）；University of Zurich（苏黎世大学）
Exploiting Dialect Identification in Automatic Dialectal Text Normalization
Computational Approaches to Modeling Language Lab（语言建模计算方法实验室）；New York University Abu Dhabi（纽约大学迪拜分校）；Carnegie Mellon University in Qatar（卡内基梅隆大学多哈分校）
MaiBaam: A Multi-Dialectal Bavarian Universal Dependency Treebank
Center for Information and Language Processing, LMU Munich（慕尼黑大学信息与语言处理中心）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；IT University of Copenhagen（哥本哈根信息技术大学）
Sonorant spectra and coarticulation distinguish speakers with different dialects
Johns Hopkins School of Medicine（约翰斯·霍普金斯大学医学院）；Cyprus University of Technology（塞浦路斯理工大学）；University of Oslo（奥斯陆大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Vashantor: A Large-scale Multilingual Benchmark Dataset for Automated Translation of Bangla Regional Dialects to Bangla Language
Department of Computer Science and Engineering, Ahsanullah University of Science and Technology（阿斯南大学科学与技术学院）；Environmental Sciences, Khulna University（库尔纳大学环境科学系）；Department of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）
RegSpeech12: A Regional Corpus of Bengali Spontaneous Speech Across Dialects
BRAC University（孟加拉国布拉克大学）；Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；Shahjalal University of Science and Technology（沙赫贾拉勒科技大学）；Khulna University（库尔纳大学）；Islamic University of Technology（伊斯兰科技大学）；Daffodil International University（达福迪尔国际大学）；Boston University（波士顿大学）；Rice University（莱斯大学）
SDS-200: A Swiss German Speech to Standard German Text Corpus
University of Applied Sciences and Arts Northwestern Switzerland（西北瑞士应用科学与艺术大学）；Zurich University of Applied Sciences（苏黎世应用科学大学）；SpinningBytes AG（SpinningBytes公司）
