Date: Monday 11/5/2012 Time: 2:15 pm Speaker: Qun Liu (Dublin City University) Venue: CUNY Graduate Center Science Center (Room 4102) Title: Context-Aware Rule-Selection for Statistical Machine Translation Abstract: All translation models use some kinds of translation rules to map a source word, phrase or structure to a target one. In current translation models the probability of this mapping are static and unaware of context, which means the rule selection in decoding is actually made by the language model or re-ordering model. In this talk we proposed an idea to incorporate context features into translation models to conduct better rule selection. This idea is compatible with the mainstream linear framework for statistical machine translation and is very suitable to incorporate various context features in a local manner without influence on the global model. We successfully implements this idea in various translation models using different context features and obtain significant improvements over baseline systems. Bio: Prof. Qun Liu is Professor of Machine Translation at Centre for Next Generation Localisation (CNGL) and in the School of Computing at Dublin City University (DCU). Before joining CNGL, Prof. Liu was a Professor and the Director of the Natural Language Processing Research Group at the Institute of Computing Technology (ICT) at Chinese Academy of Sciences (CAS) in Beijing. He got his Master degree in ICT at CAS in 1992 and his Ph.D. degree in Peking University in 2004. His research interests include Machine Translation, Human Language Technologies and Natural Language Processing for Chinese. He is the author or co-author of more than 150 research publications in the areas of Machine Translation and Natural Language Processing, including many publications at the top international ACL, COLING, EMNLP, IJCNLP and AAAI conferences. His recent researches mainly focus on linguistically enhanced statistical machine translation models and evaluation approaches.