Improving the Robustness of Dense Retrievers Against Typos via Multi-Positive Contrastive Learning
Lecture Notes in Computer Science Advances in Information Retrieval(2024)
摘要
Dense retrieval has become the new paradigm in passage retrieval. Despite its
effectiveness on typo-free queries, it is not robust when dealing with queries
that contain typos. Current works on improving the typo-robustness of dense
retrievers combine (i) data augmentation to obtain the typoed queries during
training time with (ii) additional robustifying subtasks that aim to align the
original, typo-free queries with their typoed variants. Even though multiple
typoed variants are available as positive samples per query, some methods
assume a single positive sample and a set of negative ones per anchor and
tackle the robustifying subtask with contrastive learning; therefore, making
insufficient use of the multiple positives (typoed queries). In contrast, in
this work, we argue that all available positives can be used at the same time
and employ contrastive learning that supports multiple positives
(multi-positive). Experimental results on two datasets show that our proposed
approach of leveraging all positives simultaneously and employing
multi-positive contrastive learning on the robustifying subtask yields
improvements in robustness against using contrastive learning with a single
positive.
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