Distillation for Multilingual Information Retrieval
This work addresses the challenge of training models for multilingual document ranking, which is an incremental improvement over existing cross-language retrieval methods.
The paper tackles the problem of multilingual information retrieval (MLIR) where models must rank documents across multiple languages, extending the Translate-Distill framework to propose Multilingual Translate-Distill (MTD). The result shows that ColBERT-X models trained with MTD outperform previous state-of-the-art methods by 5-25% in nDCG@20 and 15-45% in MAP.
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.