Liang Huang is Principal Scientist of Baidu Silicon Valley AI Lab (SVAIL) and Assistant Professor (on leave) at Oregon State University. He received his PhD from the University of Pennsylvania in 2008 (under the late Aravind Joshi) and BS from Shanghai Jiao Tong University in 2003. He has been a research scientist at Google, a research assistant professor at USC/ISI, an assistant professor at CUNY, a part-time research scientist at IBM, and an assistant professor at Oregon State University. He is a leading expert in natural language processing (NLP), where he is known for his work on fast algorithms and provable theory in parsing, machine translation, and structured prediction. Dr. Huang also works on applying the same linear-time algorithms he developed for parsing to computational structural biology. He received a Best Paper Award at ACL 2008, a Best Paper Honorable Mention at EMNLP 2016, several best paper nominations (ACL 2007, EMNLP 2008, and ACL 2010), two Google Faculty Research Awards (2010 and 2013), a Yahoo! Faculty Research Award (2015), and a University Teaching Prize at Penn (2005). The NLP group he created at Oregon State University ranks 16th on csrankings.org. He also enjoys teaching algorithms and co-authored a best-selling textbook in China on algorithms for programming contests.
Linear-Time Constituency Parsing with RNNs and Dynamic ProgrammingJuneki Hong and Liang Huang
We pro- pose a linear-time constituency parser with RNNs and dynamic programming using graph-structured stack and beam search, which runs in time O(nb2) where b is the beam size. We further speed this up to O(nblogb) by integrating cube prun- ing. Compared with chart parsing base- lines, this linear-time parser is substan- tially faster for long sentences on the Penn Treebank and orders of magnitude faster for discourse parsing, and achieves the highest F1 accuracy on the Penn Treebank among single model end-to-end systems.Natural Language and Speech