  Recent work in cross-language information retrieval (CLIR), where queries and
documents are in different languages, has shown the benefit of the
Translate-Distill framework that trains a cross-language neural dual-encoder
model using translation and distillation. However, Translate-Distill only
supports a single document language. Multilingual information retrieval (MLIR),
which ranks a multilingual document collection, is harder to train than CLIR
because the model must assign comparable relevance scores to documents in
different languages. This work extends Translate-Distill and propose
Multilingual Translate-Distill (MTD) for MLIR. We show that ColBERT-X models
trained with MTD outperform their counterparts trained ith Multilingual
Translate-Train, which is the previous state-of-the-art training approach, by
5% to 25% in nDCG@20 and 15% to 45% in MAP. We also show that the model is
robust to the way languages are mixed in training batches. Our implementation
is available on GitHub.
