# Sebastian, Basti, Wastl?! Recognizing Named Entities in Bavarian Dialectal Data

arXiv: https://arxiv.org/abs/2403.12749 · HTML: https://mlsanity.com/paper/2403.12749/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 74 references that are in the map.
- First posted on arXiv 19 Mar 2024.

## Builds on (most cited first, 20 of 20)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [German's Next Language Model](https://mlsanity.com/paper/2010.10906/) (2020)
- [CrossNER: Evaluating Cross-Domain Named Entity Recognition](https://mlsanity.com/paper/2012.04373/) (2020)
- [Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP](https://mlsanity.com/paper/2005.14672/) (2020)
- [Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT](https://mlsanity.com/paper/2205.09651/) (2022)
- [Named Entity Recognition in Twitter: A Dataset and Analysis on Short-Term Temporal Shifts](https://mlsanity.com/paper/2210.03797/) (2022)
- [Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task](https://mlsanity.com/paper/1906.04138/) (2019)
- [A Dataset of German Legal Documents for Named Entity Recognition](https://mlsanity.com/paper/2003.13016/) (2020)
- [L3Cube-MahaNER: A Marathi Named Entity Recognition Dataset and BERT models](https://mlsanity.com/paper/2204.06029/) (2022)
- [Parsing Tweets into Universal Dependencies](https://mlsanity.com/paper/1804.08228/) (2018)
- [DaN+: Danish Nested Named Entities and Lexical Normalization](https://mlsanity.com/paper/2105.11301/) (2021)
- [SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2)](https://mlsanity.com/paper/2305.06586/) (2023)
- [A Survey of Corpora for Germanic Low-Resource Languages and Dialects](https://mlsanity.com/paper/2304.09805/) (2023)
- [KazNERD: Kazakh Named Entity Recognition Dataset](https://mlsanity.com/paper/2111.13419/) (2021)
- [Do CoNLL-2003 Named Entity Taggers Still Work Well in 2023?](https://mlsanity.com/paper/2212.09747/) (2022)
- [A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events](https://mlsanity.com/paper/2004.03283/) (2020)
- [MaiBaam: A Multi-Dialectal Bavarian Universal Dependency Treebank](https://mlsanity.com/paper/2403.10293/) (2024)
- [A Tweet Dataset Annotated for Named Entity Recognition and Stance Detection](https://mlsanity.com/paper/1901.04787/) (2019)
- [CAp 2017 challenge: Twitter Named Entity Recognition](https://mlsanity.com/paper/1707.07568/) (2017)
- [MobIE: A German Dataset for Named Entity Recognition, Entity Linking and Relation Extraction in the Mobility Domain](https://mlsanity.com/paper/2108.06955/) (2021)

## Built on by (most cited first, 8 of 8)

- [Improving Dialectal Slot and Intent Detection with Auxiliary Tasks: A Multi-Dialectal Bavarian Case Study](https://mlsanity.com/paper/2501.03863/) (2025)
- [Konooz: Multi-domain Multi-dialect Corpus for Named Entity Recognition](https://mlsanity.com/paper/2506.12615/) (2025)
- [Make Every Letter Count: Building Dialect Variation Dictionaries from Monolingual Corpora](https://mlsanity.com/paper/2509.17855/) (2025)
- [OpenNER 1.0: Standardized Open-Access Named Entity Recognition Datasets in 50+ Languages](https://mlsanity.com/paper/2412.09587/) (2024)
- [Indirect Question Answering in English, German and Bavarian: A Challenging Task for High- and Low-Resource Languages Alike](https://mlsanity.com/paper/2603.15130/) (2026)
- [Meenz bleibt Meenz, but Large Language Models Do Not Speak Its Dialect](https://mlsanity.com/paper/2602.16852/) (2026)
- [textitVersteasch du mi? Computational and Socio-Linguistic Perspectives on GenAI, LLMs, and Non-Standard Language](https://mlsanity.com/paper/2603.28213/) (2026)
- [Can LLM Agents Identify Spoken Dialects like a Linguist?](https://mlsanity.com/paper/2603.29541/) (2026)

## Abstract (arXiv)

Named Entity Recognition (NER) is a fundamental task to extract key information from texts, but annotated resources are scarce for dialects. This paper introduces the first dialectal NER dataset for German, BarNER, with 161K tokens annotated on Bavarian Wikipedia articles (bar-wiki) and tweets (bar-tweet), using a schema adapted from German CoNLL 2006 and GermEval. The Bavarian dialect differs from standard German in lexical distribution, syntactic construction, and entity information. We conduct in-domain, cross-domain, sequential, and joint experiments on two Bavarian and three German corpora and present the first comprehensive NER results on Bavarian. Incorporating knowledge from the larger German NER (sub-)datasets notably improves on bar-wiki and moderately on bar-tweet. Inversely, training first on Bavarian contributes slightly to the seminal German CoNLL 2006 corpus. Moreover, with gold dialect labels on Bavarian tweets, we assess multi-task learning between five NER and two Bavarian-German dialect identification tasks and achieve NER SOTA on bar-wiki. We substantiate the necessity of our low-resource BarNER corpus and the importance of diversity in dialects, genres, and topics in enhancing model performance.