Deep Learning Drives Language Understanding and Beyond
|Date||03 Oct 2018 (Wednesday)|
|Time||15:30 – 16:30|
|Venue||IAS2042, Lo Ka Chung Building, Lee Shau Kee Campus, HKUST|
|Speaker||Dr. Xiaodong HE|
|Technical Vice President of JD.com|
Deputy Managing Director of JD AI Research
Head of the Deep Learning NLP and Speech Lab
Deep learning is one of the strong forces recently driving rapid progress in natural language processing (NLP) . In this talk, I will discuss recent progress in natural language processing (NLP) from two angles, including how AI system can understand humans’ intents through NLP technology, such as understanding intentions, parsing semantics, identifying emotions, and searching for recommendations; and how can AI system generate output in natural language that can be understood and accepted by humans, such as text summaries, content generation, topic development, emotional dialogue, etc. I will also explore the latest advances in the frontiers of multimodal intelligence, long text generation, emotional and stylistic expression, and human-computer dialogue, which I believe breakthroughs at these directions will greatly benefit the whole AI area, and empower the communication between humans and the real world and enables enormous scenarios such as human-like chatbot, smart city, and intelligent augmented reality.
Dr. Xiaodong HE is Technical Vice President of JD.com, Deputy Managing Director of JD AI Research, and Head of the Deep learning NLP and Speech Lab. He is also Affiliate Professor at the University of Washington (Seattle). Before joining JD.com in 2018, He served as Principal Researcher and Research Manager of the DLTC at Microsoft Research, Redmond, USA. His research interests are mainly in artificial intelligence. He has published more than 100 papers in AI areas. He received several awards including the Outstanding Paper Award at ACL 2015. He and colleagues invented the deep structured semantic model (DSSM) and the hierarchical attention Network (HAN), which are both broadly applied to language, vision, IR and knowledge representation tasks. He also led the CaptionBot project at MSR. He and colleagues has won major AI challenges including 2008 NIST MT Eval, IWSLT 2011, COCO Captioning Challenge 2015, and VQA 2017. He has held editorial positions on IEEE Journals, served as area chair or organizing committee/program committee member of major speech and language processing conferences. He was a member of the IEEE SLTC. He was the Chair of the IEEE Seattle Section in 2016. He held a bachelor degree from Tsinghua University (Beijing), MS degree from Chinese Academy of Sciences (Beijing), and the PhD degree from the University of Missouri – Columbia.