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.

这篇论文介绍了第一个巴伐利亚方言命名实体识别（NER）数据集 BarNER，使用巴伐利亚维基百科文章和推文进行注释，研究了德语西班牙标准 NER 数据集以及巴伐利亚 NER 结果的实验并提出了巴伐利亚 NER 结果的首次综合研究结果。

巴伐利亚方言数据中的命名实体识别

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

Bilingual word lexicons are crucial tools for multilingual natural language
understanding and machine translation tasks, as they facilitate the mapping of
words in one language to their synonyms in another language. To achieve this,
numerous papers have explored bilingual lexicon induction (BLI) in
high-resource scenarios, using a typical pipeline consisting of two
unsupervised steps: bitext mining and word alignment, both of which rely on
pre-trained large language models~(LLMs).
In this paper, we present an analysis of the BLI pipeline for German and two
of its dialects, Bavarian and Alemannic. This setup poses several unique
challenges, including the scarcity of resources, the relatedness of the
languages, and the lack of standardization in the orthography of dialects. To
evaluate the BLI outputs, we analyze them with respect to word frequency and
pairwise edit distance. Additionally, we release two evaluation datasets
comprising 1,500 bilingual sentence pairs and 1,000 bilingual word pairs. They
were manually judged for their semantic similarity for each Bavarian-German and
Alemannic-German language pair.

本文分析了使用双语词汇词典进行多语言自然语言理解和机器翻译任务的关键步骤：双语词汇识别（BLI）。研究了在德国及其两个方言，巴伐利亚语和阿勒曼尼亚语中进行 BLI 的挑战，使用预训练的大型语言模型（LLMs）的双语文本挖掘和词对齐。最终提供了两个评估数据集和结果分析。