@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Bi-Text Mining across German Dialects: On the Role of Synthetic Training Data for Dialect Adaptation
%A Wang, Jing
%A Plank, Barbara
%A Litschko, Robert
%Y Rapp, Reinhard
%Y Terryn, Ayla Rigouts
%Y Sharoff, Serge
%Y Zweigenbaum, Pierre
%S Proceedings of the 19th Workshop on Building and Using Comparable Corpora (BUCC)
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma de Mallorca, Spain
%F wang-etal-2026-bi
%X 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.
%R 10.63317/3gmqhegz45cn
%U https://aclanthology.org/2026.bucc-1.9/
%U https://doi.org/10.63317/3gmqhegz45cn
%P 72-83
Markdown (Informal)
[Bi-Text Mining across German Dialects: On the Role of Synthetic Training Data for Dialect Adaptation](https://aclanthology.org/2026.bucc-1.9/) (Wang et al., BUCC 2026)
ACL