# Teaching a New Dog Old Tricks: Resurrecting Multilingual Retrieval Using Zero-shot Learning

arXiv: https://arxiv.org/abs/1912.13080 · HTML: https://mlsanity.com/paper/1912.13080/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2019.

## Built on by (most cited first, 16 of 16)

- [mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset](https://mlsanity.com/paper/2108.13897/) (2021)
- [Making a MIRACL: Multilingual Information Retrieval Across a Continuum of Languages](https://mlsanity.com/paper/2210.09984/) (2022)
- [Transfer Learning Approaches for Building Cross-Language Dense Retrieval Models](https://mlsanity.com/paper/2201.08471/) (2022)
- [Towards Best Practices for Training Multilingual Dense Retrieval Models](https://mlsanity.com/paper/2204.02363/) (2022)
- [On Cross-Lingual Retrieval with Multilingual Text Encoders](https://mlsanity.com/paper/2112.11031/) (2021)
- [Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval](https://mlsanity.com/paper/2204.02292/) (2022)
- [Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval](https://mlsanity.com/paper/2101.08370/) (2021)
- [C3: Continued Pretraining with Contrastive Weak Supervision for Cross Language Ad-Hoc Retrieval](https://mlsanity.com/paper/2204.11989/) (2022)
- [Soft Prompt Decoding for Multilingual Dense Retrieval](https://mlsanity.com/paper/2305.09025/) (2023)
- [A Systematic Evaluation of Transfer Learning and Pseudo-labeling with BERT-based Ranking Models](https://mlsanity.com/paper/2103.03335/) (2021)
- [ColBERT-XM: A Modular Multi-Vector Representation Model for Zero-Shot Multilingual Information Retrieval](https://mlsanity.com/paper/2402.15059/) (2024)
- [Cross-Lingual Training with Dense Retrieval for Document Retrieval](https://mlsanity.com/paper/2109.01628/) (2021)
- [Boosting Zero-shot Cross-lingual Retrieval by Training on Artificially Code-Switched Data](https://mlsanity.com/paper/2305.05295/) (2023)
- [Towards Better Monolingual Japanese Retrievers with Multi-Vector Models](https://mlsanity.com/paper/2312.16144/) (2023)
- [Disentangled Contrastive Learning for Zero-Shot Multilingual Dense Retrieval](https://mlsanity.com/paper/2608.02189/) (2026)
- [A General-Purpose Multilingual Document Encoder](https://mlsanity.com/paper/2305.07016/) (2023)

## Abstract (arXiv)

While billions of non-English speaking users rely on search engines every day, the problem of ad-hoc information retrieval is rarely studied for non-English languages. This is primarily due to a lack of data set that are suitable to train ranking algorithms. In this paper, we tackle the lack of data by leveraging pre-trained multilingual language models to transfer a retrieval system trained on English collections to non-English queries and documents. Our model is evaluated in a zero-shot setting, meaning that we use them to predict relevance scores for query-document pairs in languages never seen during training. Our results show that the proposed approach can significantly outperform unsupervised retrieval techniques for Arabic, Chinese Mandarin, and Spanish. We also show that augmenting the English training collection with some examples from the target language can sometimes improve performance.