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.

本文介绍一种无监督训练的密集感知器，使用对比学习 (contrastive learning) 的方法，提高了信息检索的性能，并在多语言检索中表现出强大的跨语言转移能力。