Incorporating Loose-Structured Knowledge into Conversation Modeling via Recall-Gate LSTM

Understand

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.