Unsupervised Dense Information Retrieval with Contrastive Learning

Understand

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.