  Neural network models are capable of generating extremely natural sounding
conversational interactions. Nevertheless, these models have yet to demonstrate
that they can incorporate content in the form of factual information or
entity-grounded opinion that would enable them to serve in more task-oriented
conversational applications. This paper presents a novel, fully data-driven,
and knowledge-grounded neural conversation model aimed at producing more
contentful responses without slot filling. We generalize the widely-used
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.
