Unsupervised Dense Information Retrieval with Contrastive Learning

TL;DR

This paper demonstrates that contrastive learning can effectively train unsupervised dense retrievers, achieving competitive results across multiple datasets, languages, and transfer scenarios, surpassing traditional methods like BM25.

Contribution

It introduces a contrastive learning approach for unsupervised dense retrieval that performs well without large labeled datasets and enables effective cross-lingual retrieval.

Findings

Outperforms BM25 on 11/15 datasets in BEIR benchmark.

Pre-training with contrastive learning improves fine-tuned retrieval performance.

Effective in low-resource and cross-lingual retrieval scenarios.

Abstract

Recently, information retrieval has seen the emergence of dense retrievers, using neural networks, as an alternative to classical sparse methods based on term-frequency. These models have obtained state-of-the-art results on datasets and tasks where large training sets are available. However, they do not transfer well to new applications with no training data, and are outperformed by unsupervised term-frequency methods such as BM25. In this work, we explore the limits of contrastive learning as a way to train unsupervised dense retrievers and show that it leads to strong performance in various retrieval settings. On the BEIR benchmark our unsupervised model outperforms BM25 on 11 out of 15 datasets for the Recall@100. When used as pre-training before fine-tuning, either on a few thousands in-domain examples or on the large MS~MARCO dataset, our contrastive model leads to improvements on the BEIR benchmark. Finally, we evaluate our approach for multi-lingual retrieval, where training data is even scarcer than for English, and show that our approach leads to strong unsupervised performance. Our model also exhibits strong cross-lingual transfer when fine-tuned on supervised English data only and evaluated on low resources language such as Swahili. We show that our unsupervised models can perform cross-lingual retrieval between different scripts, such as retrieving English documents from Arabic queries, which would not be possible with term matching methods.