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.

SparseCL 是一种使用特殊训练的句子嵌入来有效地识别和检索与给定查询矛盾的文档的新方法。通过结合余弦相似度和稀疏函数的组合度量，此方法显着提高了矛盾检测的速度，并且在大规模文本语料库中表现出较高的准确性和效率。