SparseCL：用于矛盾检索的稀疏对比学习
SparseCL: Sparse Contrastive Learning for Contradiction Retrieval
AI总结：
本文提出 SparseCL，通过结合余弦相似度与稀疏函数的专用句子嵌入实现高效矛盾检索，并在多个数据集上取得超过30%的准确率提升。
摘要：
矛盾检索是指识别并提取明确不同意或反驳查询内容的文档，这对事实核查和数据清洗等许多下游应用都很重要。为了从大型文档语料库中检索与查询相矛盾的论点，现有方法（如相似性搜索和 crossencoder 模型）表现出显著局限。前者由于其偏好相似性的内在特性，难以捕捉矛盾的本质；后者则面临计算效率低下的问题，尤其是在语料库规模较大时。为应对这些挑战，我们提出了一种新方法：SparseCL，它利用经过专门训练的句子嵌入，旨在保留句子之间细微、矛盾的语义差别。我们的方法使用余弦相似度与稀疏函数的组合度量，以高效识别并检索与给定查询相矛盾的文档。该方法通过将穷尽式文档比较简化为简单的向量计算，大幅提升了矛盾检测速度。我们使用专门面向矛盾检索的基准数据集 Arguana，以及使用 GPT-4 从 MSMARCO 和 HotpotQA 数据集生成的合成矛盾来验证模型。实验表明，我们的方法不仅在矛盾检索中有效——在不同模型架构下，于 MSMARCO 和 HotpotQA 上取得超过30%的准确率提升——而且适用于清洗受损语料库以恢复高质量问答检索等应用。本文概述了在大规模文本语料库中提升矛盾检索准确性和效率的一个有前景方向。
英文摘要：
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.
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