Topic Aware Neural Response Generation

Understand

We consider incorporating topic information into the sequence-to-sequence framework to generate informative and interesting responses for chatbots.

• To this end, we propose a topic aware sequence-to-sequence (TA-Seq2Seq) model.
• The model utilizes topics to simulate prior knowledge of human that guides them to form informative and interesting responses in conversation, and leverages the topic information in generation by a joint attention mechanism and a biased generation probability.
• The joint attention mechanism summarizes the hidden vectors of an input message as context vectors by message attention, synthesizes topic vectors by topic attention from the topic words of the message obtained from a pre-trained LDA model, and let these vectors jointly affect the generation of words in decoding.