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Extending English IR methods to multi-lingual IR
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This paper describes our participation in the 2023 WSDM CUP - MIRACL challenge. Via a combination of i) document translation; ii) multilingual SPLADE and Contriever; and iii) multilingual RankT5 and many other models, we were able to get first place in both the known and surprise languages tracks. Our strategy mostly revolved around getting the most diverse runs for the first stage and then throwing all possible reranking techniques. While this was not a first for many techniques, we had some things that we believe were never tried before, for example, we train the first SPLADE model that is effectively capable of working in more than 10 languages. However, a more careful study of the results is needed in order to verify if we were able to get first place just due to brute force or if the hybrids we developed really brought improvements over the other team's solutions.
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Boosting Data Utilization for Multilingual Dense Retrieval
A three-stage data-utilization pipeline for multilingual dense retrieval, combining ensemble hard-negative mining, LLM-based filtering/generation, and monolingual topic-diverse mini-batches, improves MIRACL nDCG@10 by...
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