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人物画像或知识对于有依据的对话响应生成至关重要。然而，现有研究大多孤立地探讨每种依据类型，直到近期工作才引入了更具实用性的多上下文对话任务。我们将人物画像与知识双上下文识别定义为在给定对话中联合识别人物画像和知识的任务，这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依据检索方法，该方法同时利用对话的所有上下文。该方法通过采用神经问答检索模型，降低了计算资源需求。我们进一步引入了一种全新的零正例排序测试，用于衡量在语义不相似样本（即难负例）上相对于数据增强的排序性能表现。