@inproceedings{wang-etal-2026-bi,
    title = "Bi-Text Mining across {G}erman Dialects: On the Role of Synthetic Training Data for Dialect Adaptation",
    author = "Wang, Jing  and
      Plank, Barbara  and
      Litschko, Robert",
    editor = "Rapp, Reinhard  and
      Terryn, Ayla Rigouts  and
      Sharoff, Serge  and
      Zweigenbaum, Pierre",
    booktitle = "Proceedings of the 19th Workshop on Building and Using Comparable Corpora ({BUCC})",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/paragraph-normalization/2026.bucc-1.9/",
    doi = "10.63317/3gmqhegz45cn",
    pages = "72--83",
    abstract = "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."
}