English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we present our solution to the Multilingual InformationRetrieval Across a Continuum of Languages (MIRACL) challenge of WSDM CUP2023\footnote{https://project-miracl.github.io/}. Our solution focuses onenhancing the ranking stage, where we fine-tune pre-trained multilingualtransformer-based models with MIRACL dataset. Our model improvement is mainlyachieved through diverse data engineering techniques, including the collectionof additional relevant training data, data augmentation, and negative sampling.Our fine-tuned model effectively determines the semantic relevance betweenqueries and documents, resulting in a significant improvement in the efficiencyof the multilingual information retrieval process. Finally, Our team is pleasedto achieve remarkable results in this challenging competition, securing 2ndplace in the Surprise-Languages track with a score of 0.835 and 3rd place inthe Known-Languages track with an average nDCG@10 score of 0.716 across the 16known languages on the final leaderboard.

Enhancing Model Performance in Multilingual Information Retrieval with Comprehensive Data Engineering Techniques