In this paper, we present our solution to the Multilingual Information Retrieval Across a Continuum of Languages (MIRACL) challenge of WSDM CUP 2023\footnote{https://project-miracl.github.io/}. Our solution focuses on enhancing the ranking stage, where we fine-tune pre-trained multilingual transformer-based models with MIRACL dataset. Our model improvement is mainly achieved through diverse data engineering techniques, including the collection of additional relevant training data, data augmentation, and negative sampling. Our fine-tuned model effectively determines the semantic relevance between queries and documents, resulting in a significant improvement in the efficiency of the multilingual information retrieval process. Finally, Our team is pleased to achieve remarkable results in this challenging competition, securing 2nd place in the Surprise-Languages track with a score of 0.835 and 3rd place in the Known-Languages track with an average nDCG@10 score of 0.716 across the 16 known languages on the final leaderboard.

翻译：在本文中,我们介绍了我们应对WSDM CUP 2023\foot{https://project-miiracl.github.io/}WSDM CUP 2023\ footote{https://project-miracl.github.io/}的多语文信息在语言连续体中检索的多语言信息检索挑战。我们的解决方案侧重于加强排名阶段,我们用MIRACL数据集微调预先训练的多语种变压器模型。我们模型的改进主要通过多种数据工程技术来实现,包括收集更多的相关培训数据、数据增强和负面抽样。我们的微调模型有效地确定了查询和文件之间的语义相关性,从而大大提高了多语种信息检索进程的效率。最后,我们的团队很高兴在这一具有挑战性的竞争中取得显著的成果,在Surprise-Languages轨道上获得2个位,在Knn-Languages轨道上获得0.835分和第3位,在Kn-Languages轨道上获得平均0.716种已知语言的NDCG10分。