In this paper, we introduce the approach behind our submission for the MIRACL challenge, a WSDM 2023 Cup competition that centers on ad-hoc retrieval across 18 diverse languages. Our solution contains two neural-based models. The first model is a bi-encoder re-ranker, on which we apply a cross-lingual distillation technique to transfer ranking knowledge from English to the target language space. The second model is a cross-encoder re-ranker trained on multilingual retrieval data generated using neural machine translation. We further fine-tune both models using MIRACL training data and ensemble multiple rank lists to obtain the final result. According to the MIRACL leaderboard, our approach ranks 8th for the Test-A set and 2nd for the Test-B set among the 16 known languages.
@article{arxiv.2302.13400,
title = {Cross-lingual Knowledge Transfer via Distillation for Multilingual Information Retrieval},
author = {Zhiqi Huang and Puxuan Yu and James Allan},
journal= {arXiv preprint arXiv:2302.13400},
year = {2023}
}