Parse and Corpus-Based Machine Translation
Vincent Vandeghinste, Scott Martens, Gideon Kotzé, Jörg Tiedemann, Joachim Van den Bogaert, Koen De Smet, Frank Van Eynde, Gertjan van Noord · Theory and applications of natural language processing · 2012
In this paper the PaCo-MT project is described, in which Parse and Corpus-based Machine Translation has been investigated: a data-driven approach to stochastic syntactic rule-based machine translation.In contrast to the phrase-based statistical machine translation systems (PB-SMT) which are string-based and do not use any linguistic knowledge, an MT engine in a different paradigm was built: a tree-based data-driven system that automatically induces translation rules from a large syntactically analysed parallelcorpus. The architecture is presented in detail as well as an evaluation in comparison with our previous work and with the current state-of-the art PB-SMT system Moses.