Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey
Jinjie Ni Nanyang Technological University, Singapore. {jinjie001, yang0552, {vlad.pandelea, Tom Young111Equal contribution Nanyang Technological University, Singapore. {jinjie001, yang0552, {vlad.pandelea, Vlad Pandelea Nanyang Technological University, Singapore. {jinjie001, yang0552, {vlad.pandelea, Fuzhao Xue Nanyang Technological University, Singapore. {jinjie001, yang0552, {vlad.pandelea, Erik Cambria333Corresponding author Nanyang Technological University, Singapore. {jinjie001, yang0552, {vlad.pandelea,
Abstract
Dialogue systems are a popular natural language processing (NLP) task as it is promising in real-life applications. It is also a complicated task since many NLP tasks deserving study are involved. As a result, a multitude of novel works on this task are carried out, and most of them are deep learning based due to the outstanding performance. In this survey, we mainly focus on the deep learning based dialogue systems. We comprehensively review state-of-the-art research outcomes in dialogue systems and analyze them from two angles: model type and system type. Specifically, from the angle of model type, we discuss the principles, characteristics, and applications of different models that are widely used in dialogue systems. This will help researchers acquaint these models and see how they are applied in state-of-the-art frameworks, which is rather helpful when designing a new dialogue system. From the angle of system type, we discuss task-oriented and open-domain dialogue systems as two streams of research, providing insight into the hot topics related. Furthermore, we comprehensively review the evaluation methods and datasets for dialogue systems to pave the way for future research.
中文速览
对话系统（chatbot）既要听懂人说的话，又要给出合理回应，背后涉及自然语言理解、状态追踪、策略学习、文本生成等众多子任务，研究难度很高。这篇综述系统梳理了基于深度学习（deep learning）的对话系统领域的最新进展，从"模型视角"和"系统视角"两条主线展开：前者逐一介绍卷积神经网络、循环神经网络、Transformer、强化学习、生成对抗网络等主流模型的原理及其在对话中的具体用法；后者分别剖析任务型对话系统（task-oriented dialogue）和开放域对话系统（open-domain dialogue）的技术路线、热点话题与挑战，并全面整理了常用评估方法和数据集。综述还指出了若干值得关注的未来研究方向，如端到端优化、可控生成和视觉对话等。对于刚入门对话系统或希望快速掌握领域前沿的研究者来说，这篇文章提供了目前最全面、最新的参考图谱，读完可以直接上手跟进最新工作。
原文 arXiv:2105.04387;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2105.04387v5