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Neural Approaches to Conversational AI

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arxiv 1809.08267 v3 pith:OHZTI3VL submitted 2018-09-21 cs.CL

Jianfeng Gao , Michel Galley , Lihong Li This is my paper · ORCID
classification cs.CL
keywords approachesconversationalneuralagentsbeensystemsansweringcase
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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.

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Cited by 1 Pith paper

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  1. Building Task Bots with Self-learning for Enhanced Adaptability, Extensibility, and Factuality

    cs.CL 2025-08 conditional novelty 2.0 of 10

    A thesis that combines self-learning from dialog logs, schema-guided prompting, and self-aligned factuality to build task bots with minimal human intervention.

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