Contradiction retrieval refers to identifying and extracting documents that explicitly disagree with or refute the content of a query, which is important to many downstream applications like fact checking and data cleaning. To retrieve contradiction argument to the query from large document corpora, existing methods such as similarity search and crossencoder models exhibit significant limitations. The former struggles to capture the essence of contradiction due to its inherent nature of favoring similarity, while the latter suffers from computational inefficiency, especially when the size of corpora is large. To address these challenges, we introduce a novel approach: SparseCL that leverages specially trained sentence embeddings designed to preserve subtle, contradictory nuances between sentences. Our method utilizes a combined metric of cosine similarity and 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. We validate our model using the Arguana dataset, a benchmark dataset specifically geared towards contradiction retrieval, as well as synthetic contradictions generated from the MSMARCO and HotpotQA datasets using GPT-4. Our experiments demonstrate the efficacy of our approach not only in contradiction retrieval with more than 30% accuracy improvements on MSMARCO and HotpotQA across different model architectures but also in applications such as cleaning corrupted corpora to restore high-quality QA retrieval. This paper outlines a promising direction for improving the accuracy and efficiency of contradiction retrieval in large-scale text corpora.

翻译：矛盾检索旨在识别并提取与查询内容明确相悖或反驳的文档，这对于事实核查和数据清洗等众多下游应用至关重要。为从大规模文档库中检索与查询相矛盾的论点，现有方法（如相似性搜索和交叉编码器模型）存在显著局限。前者因其倾向于相似性的固有特性而难以捕捉矛盾的本质，后者则存在计算效率低下的问题，尤其在语料库规模较大时更为突出。为应对这些挑战，我们提出一种新颖方法：SparseCL，该方法利用经过特殊训练的句子嵌入，旨在保留句子间细微的矛盾性差异。我们的方法结合余弦相似度与稀疏函数构成复合度量，以高效识别并检索与给定查询相矛盾的文档。此方法通过将耗尽的文档比对简化为向量计算，极大提升了矛盾检测的速度。我们使用专为矛盾检索设计的基准数据集Arguana，以及通过GPT-4从MSMARCO和HotpotQA数据集生成的合成矛盾数据，对模型进行了验证。实验结果表明，我们的方法不仅在矛盾检索中表现优异（在MSMARCO和HotpotQA数据集上不同模型架构的准确率提升均超过30%），在清洗污染语料库以恢复高质量问答检索等应用中也展现出显著效果。本文为提升大规模文本语料库中矛盾检索的准确性与效率指明了一条前景广阔的研究路径。