Identifying relevant Persona or Knowledge for conversational systems is a critical component of grounded dialogue response generation. However, each grounding has been studied in isolation with more practical multi-context tasks only recently introduced. 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 while also requiring limited training via zero-shot inference due to compatibility with neural Q \& A retrieval models. We further analyze the hard-negative behavior of combining Persona and Dialogue via our novel null-positive rank test.

翻译：识别对话系统中的相关角色或知识是生成情境化对话回应的关键组成部分。然而，每种情境化元素此前均被孤立研究，直至近期才出现更具实用性的多上下文任务。我们将角色与知识双重上下文识别定义为：针对给定对话联合识别角色与知识的任务，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情境化检索方法，该方法可同时利用对话的所有上下文，且由于与神经问答检索模型兼容，可通过零样本推理实现有限训练。我们进一步通过新颖的空正例排名检验，分析了角色与对话结合所产生的硬负样本行为。