黄民烈简介

姓名：黄民烈

职称：副教授

电子邮件：aihuang@tsinghua.edu.cn

个人主页：http://coai.cs.tsinghua.edu.cn/hml/

教育背景

工学学士 (工程物理), 清华大学, 中国, 2000;

工学博士 (计算机科学与技术), 清华大学, 中国, 2006.

研究领域

人工智能、机器学习理论与方法，包括深度学习、强化学习等；

自然语言处理技术与方法，如语言理解、语言生成、语言匹配与推理，具体应用包括自动问答、阅读理解、对话系统、情感分析等。

研究概况

研究兴趣主要集中在人工智能与机器学习方法包括深度学习、强化学习等，自然语言处理方法与应用，包括自动问答、阅读理解、对话系统、情感分析等。主要研究语言理解、语言生成、语言匹配与推理中的科学问题，致力于解决对话系统、自动问答、阅读理解中具有挑战性的人工智能问题。曾获得汉王青年创新奖、微软合作研究奖（Microsoft Collaborative Research Award）、IJCAI-ECAI 2018杰出论文奖、CCL 2018最佳系统展示奖、NLPCC 2015最佳论文奖，2016、2017年两次入选PaperWeekly评选的最值得读10/15篇NLP论文之一；其关于情绪化聊天机器人的工作被MIT Technology Review、NVIDIA、英国卫报(The Guardian)、参考消息、新华社等媒体广泛报道，故事生成的工作被TechXplore报道。已超过60篇CCF A/B类论文发表在ACL、IJCAI、AAAI、WWW、SIGIR、EMNLP、KDD、ACM TOIS等国际顶级或主流会议及期刊上。曾担任多个国际顶级会议的领域主席或高级程序委员，如AAAI 2019、IJCAI 2019、IJCAI 2018（杰出SPC）、IJCAI 2017、ACL 2016、EMNLP 2014/2011，IJCNLP 2017等，长期担任ACM TOIS、TKDE、TPAMI、CL等顶级期刊的审稿人。 与工业界建立了广泛合作，包括微软、三星、腾讯、阿里、美团、搜狗等，2019年获得微软合作研究奖。

研究课题

国家自然科学基金: 基于图结构的文献挖掘算法与理论研究 (2009-2011);

国家自然科学基金：信息多样性与信息摘要（2013-2016）；

国家科技支撑计划：法律文本中的自然语言处理问题研究（2013-2015）；

国家973项目：社会感知数据处理的基础理论（2012-2016）；

国家自然科学基金：开放领域人机对话技术研究（2019-2022）

奖励与荣誉

清华大学优秀博士论文 (2006)；

清华大学优秀博士毕业生 (2006)；

2014年入选北京市世纪人才计划。

2018年获得“钱伟长中文信息处理科学技术奖汉王青年创新奖”

学术成果

[1].Zheng Zhang, Minlie Huang, Zhongzhou Zhao, Feng Ji, Haiqing Chen, Xiaoyan Zhu. Memory-augmented Dialogue Management for Task-oriented Dialogue Systems. ACM Transaction on Information Systems, 2019.

[2].Mantong Zhou, Minlie Huang, Xiaoyan Zhu. Story Ending Selection by Finding Hints from Pairwise Candidate Endings. IEEE Transactions on Audio, Speech and Language Processing, 2019

[3].Zheng Zhang, Lizi Liao, Minlie Huang, Xiaoyan Zhu, Tat-Seng Chua. Neural Multimodal Belief Tracker with Adaptive Attention for Dialogue Systems. the Web Conference (WWW) 2019, San Francisco, USA

[4].Hao Zhou, Minlie Huang, Yishun Mao, Changlei Zhu, Peng Shu, Xiaoyan Zhu. Domain-Constrained Advertising Keyword Generation. the Web Conference (WWW) 2019, San Francisco, USA

[5].Ryuichi Takanobu, Tao Zhuang, Minlie Huang, Jun Feng, Haihong Tang, Bo Zheng. Aggregating E-commerce Search Results from Heterogeneous Sources via Hierarchical Reinforcement Learning. the Web Conference (WWW) 2019, San Francisco, USA

[6].Zhouxing Shi, Minlie Huang. A Deep Sequential Model for Discourse Parsing on Multi-Party Dialogues. AAAI 2019, Honolulu, Hawaii, USA

[7].Jian Guan, Yansen Wang, Minlie Huang. Story Ending Generation with Incremental Encoding and Commonsense Knowledge. AAAI 2019, Honolulu, Hawaii, USA

[8].Takanobu Ryuichi, Tianyang Zhang, Jiexi Liu, Minlie Huang. A Hierarchical Framework for Relation Extraction with Reinforcement Learning. AAAI 2019, Honolulu, Hawaii, USA

[9].Hao Zhou, Tom Yang, Minlie Huang, Haizhou Zhao, Jingfang Xu, Xiaoyan Zhu. Commonsense Knowledge Aware Conversation Generation with Graph Attention. IJCAI-ECAI 2018, Stockholm, Sweden. [IJCAI2018 Distinguished Paper]

[10].Hao Zhou, Minlie Huang, Tianyang Zhang, Xiaoyan Zhu, Bing Liu. Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory. AAAI 2018, New Orleans, Louisiana, USA.

[11].Pei Ke, Jian Guan, Minlie Huang, Xiaoyan Zhu. Generating Informative Responses with Controlled Sentence Function. ACL 2018, Melbourne, Australia.

[12].Qiao Qian, Minlie Huang, Haizhou Zhao, Jingfang Xu, Xiaoyan Zhu. Assigning personality/identity to a chatting machine for coherent conversation generation. IJCAI-ECAI 2018, Stockholm, Sweden.

[13].Ryuichi Takanobu, Minlie Huang, Zhongzhou Zhao, Fenglin Li, Haiqing Chen, Xiaoyan Zhu, Liqiang Nie. A Weakly Supervised Method for Topic Segmentation and Labeling in Goal-oriented Dialogues via Reinforcement Learning. IJCAI-ECAI 2018, Stockholm, Sweden.

[14].Tianyang Zhang, Minlie Huang, Li Zhao. Learning Structured Representation for Text Classification via Reinforcement Learning. AAAI 2018, New Orleans, Louisiana, USA.

[15].Yansen Wang, Chenyi Liu, Minlie Huang, Liqiang Nie. Learning to Ask Questions in Open-domain Conversational Systems with Typed Decoders. ACL 2018, Melbourne, Australia.

[16].Minlie Huang, Qiao Qian, Xiaoyan Zhu. Encoding Syntactic Knowledge in Neural Networks for Sentiment Classification. ACM Trans. Inf. Syst. 35, 3, Article 26 (June 2017), 27 pages.

[17].Qiao Qian, Minlie Huang, Jinhao Lei, Xiaoyan Zhu. Linguistically Regularized LSTMs for Sentiment Classification. ACL 2017.

[18].Han Xiao, Minlie Huang, Lian Meng, Xiaoyan Zhu. SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions. AAAI 2017. February 4–9, San Francisco, US.

[19].Yequan Wang, Minlie Huang, Li Zhao, Xiaoyan Zhu. Attention-based LSTM for Aspect-level Sentiment Classification. EMNLP 2016, Austin, Texas, USA.

[20].Han Xiao, Minlie Huang, Xiaoyan Zhu. TransG: A Generative Model for Knowledge Graph Embedding. ACL 2016, Berlin, Germany.

[21].Biao Liu, Minlie Huang, Song Liu, Xuan Zhu, Xiaoyan Zhu. A Sentence Interaction Network for Modeling Dependence between Sentences. ACL 2016, Berlin, Germany.

[22].Han Xiao, Minlie Huang, Xiaoyan Zhu. From One Point to A Manifold: Knowledge Graph Embedding For Precise Link Prediction. IJCAI 2016, New York, USA.

[23].Li Zhao, Minlie Huang, Ziyu Yao, Rongwei Su, Yingying Jiang, Xiaoyan Zhu. Semi-Supervised Multinomial Naive Bayes for Text Classification by Leveraging Word-Level Statistical Constraint. AAAI 2016, Phoenix, Arizona, USA.