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使用自然语言处理生成问题

发布日期:2022-01-06      新闻来源:       责任编辑:沙艳      地点:腾讯会议(ID: 282290675)                日期:2022.1.7

学术报告-Question Generation using Natural Language Processing

报告题目:Question Generation using Natural Language Processing

报告专家:See-Kiong Ng(黄思强)新加坡国立大学计算学院教授

报告时间:202217 上午10:00

报告地点:腾讯会议(ID: 282290675)

主办单位:中国矿业大学 计算机科学与技术学院

欢迎广大师生参加!  

Abstract:

Question generation is an important skill for chatbots in order to engage in human-like conversations. It involves learning “what to ask” and “how to ask” given an input passage: the former requires the ability to find the salient information in the input passage to ask questions about, while the latter involves generating an answer-related question given a selected salient information as the target answer.  In this talk, we present our recent works on learning what to ask relevantly using domain-centric content selection for an art docent application, and how to ask differently with fact-infused question paraphrasing.


Speaker bio:

See-Kiong Ng (Ph.D. Carnegie Mellon University) is a Professor of Practice at the Department of Computer Science of the School of Computing at National University of Singapore (NUS), and the Deputy Director of the university’s Institute of Data Science. See-Kiong obtained his Bachelor (1989), Masters (1993), and Ph.D. (1998) degrees in Computer Science from Carnegie Mellon University and a Masters (1991) degree in Artificial Intelligence from University of Pennsylvania. Prior to joining NUS in 2016, See-Kiong was a Programme Director of the Urban Systems Initiative by the Science and Engineering Research Council of the Agency of Science, Technology and Research (A*STAR), and the founding head and principle scientist of its Data Mining Department. Currently, See-Kiong’s mission at NUS is to leverage his data science and AI expertise for transdisciplinary and translational research into important real-life problems and education of the next generation of data scientists. From using the computation of data to better understand the biology of the human body, See-Kiong is using machine learning and artificial intelligence to understand the “biology” of complex human cities and societies and creating real-world impact with the science of data.

讲题:使用自然语言处理生成问题

讲座大纲

问题生成是聊天机器人进行类似人类对话的一项重要技能。 它涉及在给定输入段落的情况下学习“问什么”和“如何问”:前者需要能够找到输入段落中显著的信息以提出问题,而后者则涉及在给定一个选定显著信息作为目标答案的情况下生成与答案相关的问题。在本次讨论中,我们展示了我们最近的工作,对于艺术讲解员的应用程序,涉及使用以领域为中心的内容选择学习怎样去问,以及如何通过注入事实的问题释义进行不同的提问。

主讲人简介:

黄思强博士为新加坡国立大学计算学院计算机科学系教授,新加坡国立大学数据科学研究所副所长。1986年,他获得新加坡国家电脑局海外奖学金赴美国学习,先后获得卡内基梅隆大学的计算机科学学士(1989年)、硕士(1993年)和博士(1998年)学位,以及宾夕法尼亚大学的人工智能硕士学位(1991年)。黄博士曾担任新加坡科技研究局(A*STAR)组织的城市体系计划主任,并担任了新科研信息通信研究所数据分析部门的创始负责人和首席科学家。目前,黄博士于国大新设立的数据科学研究所,着力为新加坡培养新一代杰出的数据科学家,并与多个行业与公共机构进行成功的研究合作,以数据科学开发实用的人工智能技术。

作为一名经验丰富的数据科学家,从使用数据挖掘和机器学习来揭示人体的生物学(生物信息学),到使用大数据和人工智能来理解复杂的人类城市(智能城市),黄博士以数据的科学与应用证明了数据中的巨大价值。