Abstract

In this talk, I start with a brief introduction to the history of symbolic approaches to natural language processing (NLP), and why we move to neural approaches recently. Then I describes in detail the deep learning technologies that are recently developed for two areas of NLP tasks. First is a set of neural attention and inference models developed for machine reading comprehension and question answering. Second is the use of deep learning for various of dialogue agents, including task-completion bots and social chat bots.

Biography

Jianfeng Gao is Partner Research Manager in Deep Learning Technology Center (DLTC) at Microsoft Research, Redmond. He works on deep learning for text and image processing and leads the development of AI systems for machine reading comprehension (MRC), question answering (QA), dialogue, and business applications. From 2006 to 2014, he was Principal Researcher at Natural Language Processing Group at Microsoft Research, Redmond, where he worked on Web search, query understanding and reformulation, ads prediction, and statistical machine translation. From 2005 to 2006, he was a research lead in Natural Interactive Services Division at Microsoft, where he worked on Project X, an effort of developing natural user interface for Windows. From 2000 to 2005, he was Research Lead in Natural Language Computing Group at Microsoft Research Asia. He, together with his colleagues, developed the first Chinese speech recognition system released with Microsoft Office, the Chinese/Japanese Input Method Editors (IME) which were the leading products in the market, and the natural language platform for Windows Vista.