Distillation for Multilingual Information Retrieval
<|reference_start|>Distillation for Multilingual Information Retrieval: 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.<|reference_end|>
@article{yang2024distillation,
    title={Distillation for Multilingual Information Retrieval},
    author={Eugene Yang and Dawn Lawrie and James Mayfield},
    journal={arXiv preprint arXiv:2405.00977},
    year={2024},
    doi={10.1145/3626772.3657955},
    archivePrefix={arXiv},
    eprint={2405.00977},
    primaryClass={cs.IR cs.CL}
}