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:确定对话系统的相关人或知识是建立有根有据的对话回应机制的一个关键组成部分。然而,每个基础都是在孤立地研究的,只有最近才引入了更实用的多文本任务。我们把人和知识双重背景识别定义为为特定对话共同确定人和知识的任务,这在复杂的多文本对话环境中可能具有更重要的意义。我们开发了一种新的基础检索方法,同时利用所有对话背景,同时要求通过零点推理进行有限的培训,因为与神经QQQQ ⁇ 检索模型兼容。我们通过我们的新式的无效等级测试进一步分析人与对话相结合的硬反行为。