Multi-Positive Contrastive Learning for Typo-Robust Dense Retrieval on the BEIR Benchmark

Abstract

Abstract: Dense retrieval is becoming one of the standard approaches for document and passage ranking. The dual-encoder architecture is widely adopted for scoring question-passage pairs due to its efficiency and high performance. Typically, dense retrieval models are evaluated on clean and curated datasets. However, when deployed in real-life applications, these models encounter noisy user-generated text. That said, the performance of state-of-the-art dense retrievers can substantially deteriorate when exposed to noisy text. In this work, we study the robustness of dense retrievers against typos in the

Research Question

How does the multi-positive contrastive learning approach for typo robustness affect the performance of dense retrieval models on the BEIR benchmark when evaluated using nDCG@10 and MRR scores?

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