A Knowledge-Grounded Neural Conversation Model

Neural network models are capable of generating extremely natural sounding conversational interactions. However, these models have been mostly applied to casual scenarios (e. g. , as “chatbots”) and have yet to demonstrate they can serve in more useful conversational applications. This paper presents a novel, fully data-driven, and knowledge-grounded neural conversation model aimed at producing more contentful responses. We generalize the widely-used Sequence-to- Sequence (SEQ2SEQ) approach by conditioning responses on both conversation history and external “facts”, allowing the model to be versatile and applicable in an open-domain setting. Our approach yields significant improvements over a competitive SEQ2SEQ baseline. Human judges found that our outputs are significantly more informative.

Authors

• Marjan Ghazvininejad
• Chris Brockett
• Ming-Wei Chang
• Bill Dolan
• Jianfeng Gao
• Wen-tau Yih
• Michel Galley

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