  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.
