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.

使用Translate-Distill框架进行跨语言信息检索 (CLIR) 已经显示出其通过翻译和塑造训练跨语言神经双重编码器模型的好处。然而，Translate-Distill仅支持单一语言。本研究扩展Translate-Distill并提出了用于多语言信息检索 (MLIR) 的多语言Translate-Distill (MTD) 方法。我们通过nDCG@20指标和MAP指标在性能上展示了使用MTD培训的ColBERT-X模型优于以前的最先进训练方法Multilingual Translate-Train，其在nDCG@20上提高了5%到25%，在MAP上提高了15%到45%。我们还展示了该模型对于训练批次中语言混合方式的鲁棒性。我们的实现可在GitHub上找到。

多语言信息检索的蒸馏