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Prior work on English monolingual retrieval has shown that a cross-encodertrained using a large number of relevance judgments for query-document pairscan be used as a teacher to train more efficient, but similarly effective,dual-encoder student models. Applying a similar knowledge distillation approachto training an efficient dual-encoder model for Cross-Language InformationRetrieval (CLIR), where queries and documents are in different languages, ischallenging due to the lack of a sufficiently large training collection whenthe query and document languages differ. The state of the art for CLIR thusrelies on translating queries, documents, or both from the large English MSMARCO training set, an approach called Translate-Train. This paper proposes analternative, Translate-Distill, in which knowledge distillation from either amonolingual cross-encoder or a CLIR cross-encoder is used to train adual-encoder CLIR student model. This richer design space enables the teachermodel to perform inference in an optimized setting, while training the studentmodel directly for CLIR. Trained models and artifacts are publicly available onHuggingface.

Translate-Distill: Learning Cross-Language Dense Retrieval by Translation and Distillation