Motivated by the reasoning network for document comprehension (Shen et al., 2016), we propose multi-turn reasoning neural networks that generate the proper response (or, "answer") by attention-based reasoning from current conversation session ("document") and current query (identical to "question" in document comprehension) from the user.
our networks utilize conversation context and explicitly separate speakers' interventions into sentence-level and conversation-level memories.