Neural Approaches to Conversational AI

The present paper surveys neural approaches to conversational AI that have been developed in the last few years. We group conversational systems into three categories: (1) question answering agents, (2) task-oriented dialogue agents, and (3) chatbots. For each category, we present a review of state-of-the-art neural approaches, draw the connection between them and traditional approaches, and discuss the progress that has been made and challenges still being faced, using specific systems and models as case studies.1

Introduction. Developing an intelligent dialogue system1 that not only emulates human conversation, but also answers questions on topics ranging from latest news about a movie star to Einstein’s theory of relativity, and fulfills complex tasks such as travel planning, has been one of the longest running goals in AI. The goal has remained elusive until recently. We are now observing promising results both in academia sindustry, as large amounts of conversational data become available for training, and the breakthroughs in deep learning (DL) and reinforcement learning (RL) are applied to conversational AI. Conversational AI is fundamental to natural user interfaces. It is a rapidly growing field, attracting many researchers in the Natural Language Processing (NLP), Information Retrieval (IR) and Machine Learning (ML) communities. For example, SIGIR 2018 has created a new track of Artificial Intelligence, Semantics, and Dialog to bridge research in AI and IR, especially targeting Question Answering (QA), deep semantics and dialogue with intelligent agents.

Discussion / Conclusion. Conversational AI is a rapidly growing field. This paper surveys neural approaches that were recently developed. Some of them have already been widely used in commercial systems. We have discussed some of the main challenges in conversational AI, common to Question Answering agents, task-oriented dialogue bots and chatbots.