# A Knowledge-Grounded Neural Conversation Model

## TL;DR

This paper introduces a neural conversation model that incorporates external factual knowledge to generate more informative and content-rich responses, enhancing task-oriented dialogue capabilities.

## Contribution

It presents a novel, fully data-driven, knowledge-grounded neural conversation model that generalizes Seq2Seq by conditioning on both dialogue history and external facts.

## Key findings

- Significant improvements over baseline models
- Outputs are more informative according to human judges
- Model applicable in open-domain conversational settings

## Abstract

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.