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Bi-Text Mining across German Dialects: On the Role of Synthetic Training Data for Dialect Adaptation

Cross-dialect bi-text mining relies on robust multilingual sentence representations to identify semantically equivalent sentence pairs across languages. While recent multilingual bi-encoder models achieve strong performance on standardized written languages, their behavior on dialectal varieties is largely unknown. In this study, we use Tatoeba to evaluate the performance of four widely-used bi-encoders on dialect-to-standard German translation retrieval, covering German documents and queries written in three dialects: Low German, Bavarian, and Alemannic. Motivated by the lack of resources, we examine the extent to which synthetic translations (from dictionaries and large language models; LLMs) can serve as weak supervision for dialect adaptation. Our results reveal that bi-encoders, when applied in a zero-shot setting, exhibit deficiencies in capturing semantic similarity between German and dialects, while fine-tuning on synthetic data substantially improves their retrieval effectiveness, with larger gains obtained from LLM-translated training data. We further analyze retrieval performance on Bavarian across varying dialect word proportions and observe a drop when dialect words make up more than 60% of the text.