Slav Petrov. Building a Large-Scale Machine Translation System
Abstract: While being far from perfect, statistical machine translation systems have exhibited impressive quality improvements over the last decade. These gains have been made possible by better modeling techniques as well as larger amounts of training data. This ever growing demand for more data brings significant processing challenges with it. In this talk I will highlight some of the more important technological breakthroughs that were needed in order to build a large-scale machine translation system that can handle hundreds of language pairs and millions of daily requests.
Bio: Slav Petrov is a Research Scientist at Google New York, working on problems at the intersection of natural language processing and machine learning. He is particularly interested in syntactic parsing and its applications to machine translation and information extraction. He has published over 20 publications at international conferences and recently received the Best Paper Award at ACL’11. He was also part of the team that won the world championship in robotic soccer at RoboCup’04. He holds a PhD from UC Berkeley and a Master’s degree from the Free University of Berlin.
Petr Pleshachkov. xDB: native XML database management system (internals overview)
Bio: PhD, Principal Software Engineer at EMC. The area of interests includes all aspects of XML management systems: transaction management, storing techniques, indexing, full-text search, distributed data management, query optimization.
Pavel Velikhov. Scientific challenges to database systems and SciDB
Bio: CEO at Scientitfic Database Management Company. Research Scientist at NIISI RAS