Improving Knowledge-Aware Dialogue Generation via Knowledge Base Question Answering

Neural network models usually suffer from the challenge of incorporating commonsense knowledge into the opendomain dialogue systems. In this paper, we propose a novel knowledge-aware dialogue generation model (called TransDG), which transfers question representation and knowledge matching abilities from knowledge base question answering (KBQA) task to facilitate the utterance understanding and factual knowledge selection for dialogue generation. In addition, we propose a response guiding attention and a multi-step decoding strategy to steer our model to focus on relevant features for response generation. Experiments on two benchmark datasets demonstrate that our model has robust superiority over compared methods in generating informative and fluent dialogues. Our code is available at https: //github. com/siat-nlp/TransDG.

Authors

• Jian Wang
• Junhao Liu University of California, Irvine
• Wei Bi
• Xiaojiang Liu
• Kejing He
• Ruifeng Xu Harbin Institute of Technology, Shenzhen, China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Shenzhen, China Peng Cheng Laboratory, Shenzhen, China
• Min Yang Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences, Shenzhen, China

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