Contradiction Retrieval via Contrastive Learning with Sparsity
Abstract
Lay Summary
Contradiction retrieval refers to identifying and extracting documents that explicitly disagree with or refute the content of a query, which is crucial for downstream applications such as fact-checking and data cleaning. To tackle these challenges, we introduce SparseCL, a novel approach that utilizes specially trained sentence embeddings designed to capture subtle, contradictory nuances between sentences. Our method combines cosine similarity with a sparsity function to efficiently identify and retrieve documents that contradict a given query. This approach dramatically enhances the speed of contradiction detection by reducing the need for exhaustive document comparisons to simple vector calculations. This paper outlines a promising direction for non-similarity-based information retrieval, which is currently underexplored.