学习对话式AI:一项调查

Tingchen Fu , Shen Gao , Xueliang Zhao , Ji-rong Wen , Rui Yan
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引用次数: 15

摘要

近年来,人们对开放域对话领域的兴趣激增。由于社交媒体的快速发展,来自互联网的大量对话语料库为数据驱动的对话模型奠定了基础前提。神经网络的突破也给人工智能和自然语言处理的研究人员带来了新的思路。因此产生了大量的新技术和新方法。在本文中,我们回顾了近年来一些最具代表性的作品,并将现有的主流对话模式框架分为三类。进一步分析了开放域对话的发展趋势,并从信息和可控两个方面总结了开放域对话系统的目标。本文所综述的方法都是根据各自独特的视角来选择的,并不完整。相反,我们希望这个服务可以使NLP社区在未来的开放领域对话研究中受益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learning towards conversational AI: A survey

Recent years have witnessed a surge of interest in the field of open-domain dialogue. Thanks to the rapid development of social media, large dialogue corpus from the Internet builds up a fundamental premise for data-driven dialogue model. The breakthrough in neural network also brings new ideas to researchers in AI and NLP. A great number of new techniques and methods therefore came into being. In this paper, we review some of the most representative works in recent years and divide existing prevailing frameworks for a dialogue model into three categories. We further analyze the trend of development for open-domain dialogue and summarize the goal of an open-domain dialogue system in two aspects, informative and controllable. The methods we review in this paper are selected according to our unique perspectives and by no means complete. Rather, we hope this servery could benefit NLP community for future research in open-domain dialogue.

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