# Cross-lingual Knowledge Transfer via Distillation for Multilingual Information Retrieval

arXiv: https://arxiv.org/abs/2302.13400 · HTML: https://mlsanity.com/paper/2302.13400/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2023.

## Built on by (most cited first, 2 of 2)

- [Merging Language and Domain Specific Models: The Impact on Technical Vocabulary Acquisition](https://mlsanity.com/paper/2502.12001/) (2025)
- [LAMAR: An Open Language-Aware Multilingual Alignment Reranker](https://mlsanity.com/paper/2607.22042/) (2026)

## Abstract (arXiv)

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.