Neural Approaches to Conversational AI

TL;DR

This survey reviews recent neural methods in conversational AI, categorizing systems into question answering, task-oriented dialogue, and chatbots, highlighting advances, challenges, and connections to traditional approaches.

Contribution

It provides a comprehensive overview of state-of-the-art neural conversational systems, connecting them with traditional methods and identifying ongoing challenges.

Findings

Neural approaches have significantly advanced question answering and chatbots.

Progress has been made in integrating neural models with traditional dialogue systems.

Challenges remain in handling complex, multi-turn conversations effectively.

Abstract

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.