  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.
