Identifying relevant persona or knowledge for conversational systems is critical to grounded dialogue response generation. However, each grounding has been mostly researched in isolation with more practical multi-context dialogue tasks introduced in recent works. We define Persona and Knowledge Dual Context Identification as the task to identify persona and knowledge jointly for a given dialogue, which could be of elevated importance in complex multi-context dialogue settings. We develop a novel grounding retrieval method that utilizes all contexts of dialogue simultaneously. Our method requires less computational power via utilizing neural QA retrieval models. We further introduce our novel null-positive rank test which measures ranking performance on semantically dissimilar samples (i.e. hard negatives) in relation to data augmentation.

翻译：识别对话系统中相关的角色特征或知识库对于生成接地对话回应至关重要。然而，现有研究大多单独探索每种接地方式，而近期工作引入了更具实用性的多上下文对话任务。我们定义了"角色特征与知识双上下文识别"任务，旨在针对特定对话同时识别角色特征和知识库，该任务在复杂的多上下文对话场景中具有重要价值。我们提出了一种新颖的接地检索方法，可同时利用对话的所有上下文信息。该方法通过采用基于神经网络的问答检索模型，显著降低了计算资源需求。此外，我们创新性地引入空值正样本排名测试，该测试通过数据增强手段，衡量语义不相似样本（即硬负样本）上的排名性能。