Hierarchical Recurrent Attention Network for Response Generation

Understand

We study multi-turn response generation in chatbots where a response is generated according to a conversation context.

• Existing work has modeled the hierarchy of the context, but does not pay enough attention to the fact that words and utterances in the context are differentially important.
• As a result, they may lose important information in context and generate irrelevant responses.
• We propose a hierarchical recurrent attention network (HRAN) to model both aspects in a unified framework.