  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.
