学习对话式AI:一项调查

Tingchen Fu , Shen Gao , Xueliang Zhao , Ji-rong Wen , Rui Yan
{"title":"学习对话式AI:一项调查","authors":"Tingchen Fu ,&nbsp;Shen Gao ,&nbsp;Xueliang Zhao ,&nbsp;Ji-rong Wen ,&nbsp;Rui Yan","doi":"10.1016/j.aiopen.2022.02.001","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"3 ","pages":"Pages 14-28"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666651022000079/pdfft?md5=a8c5cdae822d93f7d82a0ff336415b53&pid=1-s2.0-S2666651022000079-main.pdf","citationCount":"15","resultStr":"{\"title\":\"Learning towards conversational AI: A survey\",\"authors\":\"Tingchen Fu ,&nbsp;Shen Gao ,&nbsp;Xueliang Zhao ,&nbsp;Ji-rong Wen ,&nbsp;Rui Yan\",\"doi\":\"10.1016/j.aiopen.2022.02.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":100068,\"journal\":{\"name\":\"AI Open\",\"volume\":\"3 \",\"pages\":\"Pages 14-28\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2666651022000079/pdfft?md5=a8c5cdae822d93f7d82a0ff336415b53&pid=1-s2.0-S2666651022000079-main.pdf\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"AI Open\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666651022000079\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"AI Open","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666651022000079","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
45.00
自引率
0.00%
发文量
0
期刊最新文献
GPT understands, too Adaptive negative representations for graph contrastive learning PM2.5 forecasting under distribution shift: A graph learning approach Enhancing neural network classification using fractional-order activation functions CPT: Colorful Prompt Tuning for pre-trained vision-language models
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1