Modeling human conversations is the essence for building satisfying chat-bots
with multi-turn dialog ability. Conversation modeling will notably benefit from
domain knowledge since the relationships between sentences can be clarified due
to semantic hints introduced by knowledge. In this paper, a deep neural network
is proposed to incorporate background knowledge for conversation modeling.
Through a specially designed Recall gate, domain knowledge can be transformed
into the extra global memory of Long Short-Term Memory (LSTM), so as to enhance
LSTM by cooperating with its local memory to capture the implicit semantic
relevance between sentences within conversations. In addition, this paper
introduces the loose structured domain knowledge base, which can be built with
slight amount of manual work and easily adopted by the Recall gate. Our model
is evaluated on the context-oriented response selecting task, and experimental
results on both two datasets have shown that our approach is promising for
modeling human conversations and building key components of automatic chatting
systems.

本文提出了深度神经网络结合领域知识来加强人类对话模型的方法，通过特定设计的回想门模型，将领域知识转化为 LSTM 的额外全局记忆来协作捕捉对话句子之间的语义相关性。同时，我们还介绍了一种松散结构的领域知识库来辅助我们的方法，实验结果表明这种方法在选择定向回应任务上表现良好，非常适合用于训练自动聊天系统。