# Neural Approaches to Conversational AI

Source: [Neural Approaches to Conversational AI](<https://www.sota2.com/research/paper/68fa66541e209131cea0f811>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:47:30.843Z

Authors: Jianfeng Gao, Michel Galley, Lihong Li

Published: 2018-09-21T18:42:24.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1809.08267>)

[PDF](<https://arxiv.org/pdf/1809.08267.pdf>)