Adapting Multilingual Embedding Models to Historical Luxembourgish
This work addresses the challenge of semantic search for historical texts in a low-resource language, which is incremental as it adapts existing methods to new data.
This study tackled the problem of poor cross-lingual semantic search for historical Luxembourgish texts by adapting multilingual embedding models using contrastive learning or knowledge distillation, resulting in significant accuracy increases for all models.
The growing volume of digitized historical texts requires effective semantic search using text embeddings. However, pre-trained multilingual models face challenges with historical content due to OCR noise and outdated spellings. This study examines multilingual embeddings for cross-lingual semantic search in historical Luxembourgish (LB), a low-resource language. We collect historical Luxembourgish news articles from various periods and use GPT-4o for sentence segmentation and translation, generating 20,000 parallel training sentences per language pair. Additionally, we create a semantic search (Historical LB Bitext Mining) evaluation set and find that existing models perform poorly on cross-lingual search for historical Luxembourgish. Using our historical and additional modern parallel training data, we adapt several multilingual embedding models through contrastive learning or knowledge distillation and increase accuracy significantly for all models. We release our adapted models and historical Luxembourgish-German/French/English bitexts to support further research.