Question Answering

Natural-language question answering (QA) has clear practical and scientific values, such as evaluating a machine’s understanding of a domain, or providing succinct and precise answers to search engine queries. While both Bing and Google have incorporated more “semantics” to return direct answers to queries, QA is far from solved, and is becoming more important as natural language interaction becomes popular (e.g., Siri and Cortana). In this session, we invite experts from both academia and Microsoft to present recent technologies for improving QA. The topics include traditional IR approaches for QA, machine reading for knowledge acquisition and representation, and semantic parsing for answering questions using structured databases like Freebase or Satori. In addition, we invite product groups (Bing QnA team) to discuss the technical challenges faced in the real-world scenarios and highlight the research need.

Speaker Details

Matthew Richardson is a principal researcher at Microsoft Research. His research includes work on natural language processing, machine learning, data mining, and extracting knowledge from large scale datasets such as query logs. His current work is focused on question answering and machine comprehension. He received his B.S. from Caltech, and a Ph.D. in Computer Science from the University of Washington in 2004. While at Microsoft, he has published papers at a wide variety of conferences, including EMNLP, AAAI, KDD, SIGIR, and WWW, won a best paper award at CHI2011, co-organized the SIGIR 2009 workshop on IR for advertising, and has served on 50 program committees.

Yan Ke is a principal software engineering manager in the Bing Experiences group, working on entity understanding and question answering. His team developed many of the ranking algorithms for the entity pane, carousel, and fact answer triggering. Previously, he developed the Web Index Discovery and Selection algorithms for Bing. Yan joined Microsoft in 2008 after graduating with his Ph.D. in Computer Science at Carnegie Mellon University, specializing in computer vision.

Benjamin Van Durme is the lead of Natural Language Understanding research at the Johns Hopkins University Human Language Technology Center of Excellence (HLTCOE), as well as an assistant research professor in both Computer Science and Cognitive Science at Johns Hopkins University. Between the HLTCOE, his small army of PhD students, and affiliated post-doctoral researchers, research areas covered include: information extraction and knowledge base population, streaming and randomized algorithms in HLT, topic modeling, author attribute prediction in social media, educational NLP, and various aspects of computational semantics.

Scott Wen-tau Yih is a senior researcher at Microsoft Research Redmond. His research interests include natural language processing, machine learning and information retrieval. Yih’s current work focuses on continuous semantic representations using neural networks and matrix/tensor decomposition methods, with applications in lexical semantics, knowledge base embedding and question answering. Yih received the best paper award from CoNLL-2011 and has served as area chairs (HLT-NAACL-12, ACL-14) and program co-chairs (CEAS-09, CoNLL-14) in recent years. Yih received his Ph.D. from the University of Illinois at Urbana-Champaign, before joining Microsoft Research.

Luke Zettlemoyer is an assistant professor in computer science and engineering at the University of Washington. He does research in natural language processing, with a focus on empirical computational semantics. Honors include paper awards at UAI and ACL, selection to the DARPA CSSG, an NSF CAREER Award, and an Allen Distinguished Investigator Award. Luke received his PhD from MIT and was a postdoc at the University of Edinburgh.

Series:: Microsoft Research Faculty Summit
Date:: July 24, 2015
Speakers:: Benjamin Van Durme, Luke Zettlemoyer, Matthew Richardson, Scott Yih, and Yan Ke
Affiliation:: Microsoft Research, Microsoft, Johns Hopkins University, University of Washington