A simple and efficient dialogue generation model incorporating commonsense knowledge

Abstract

The performance of dialogue systems, artificial intelligence systems that communicate between a user and a machine, have rapidly improved owing to the development of the pre-trained language model that can perform well in various contexts. However, dialogue systems are often not well received by users because they generate universal and formulaic answers to user queries. As a result, users turn away from dialogue systems by reducing their interest in dialogue systems and lowering their expectations. To address this limitation, we propose a simple and efficient dialogue system that uses commonsense-based language models to facilitate more natural communication. Our model determines the context of human–machine conversations and then applies the relevant commonsense embedding to user queries. We quantitatively confirmed that our dialogue system performs significantly better on Korean datasets than non-commonsense-based dialogue systems.