Training dense passage representations via contrastive learning has been
shown effective for Open-Domain Passage Retrieval (ODPR). Existing studies
focus on further optimizing by improving negative sampling strategy or extra
pretraining. However, these studies keep unknown in capturing passage with
internal representation conflicts from improper modeling granularity. This work
thus presents a refined model on the basis of a smaller granularity, contextual
sentences, to alleviate the concerned conflicts. In detail, we introduce an
in-passage negative sampling strategy to encourage a diverse generation of
sentence representations within the same passage. Experiments on three
benchmark datasets verify the efficacy of our method, especially on datasets
where conflicts are severe. Extensive experiments further present good
transferability of our method across datasets.

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。