Training dense passage representations via contrastive learning (CL) has been shown effective for Open-Domain Passage Retrieval (ODPR). Recent studies mainly focus on optimizing this CL framework by improving the sampling strategy or extra pretraining. Different from previous studies, this work devotes itself to investigating the influence of conflicts in the widely used CL strategy in ODPR, motivated by our observation that a passage can be organized by multiple semantically different sentences, thus modeling such a passage as a unified dense vector is not optimal. We call such conflicts Contrastive Conflicts. In this work, we propose to solve it with a representation decoupling method, by decoupling the passage representations into contextual sentence-level ones, and design specific CL strategies to mediate these conflicts. Experiments on widely used datasets including Natural Questions, Trivia QA, and SQuAD verify the effectiveness of our method, especially on the dataset where the conflicting problem is severe. Our method also presents good transferability across the datasets, which further supports our idea of mediating Contrastive Conflicts.

本文提出改进内部表示粒度，引入基于上下文句子的模型和 in-passage 负采样策略，以缓解不合适的建模方式对于对话的影响，在三个基准数据集上进行实验并验证方法的有效性，特别是在冲突严重的数据集中的表现。

面向开放域段落检索的句子感知对比学习