Paper: Statistical Machine Translation Part II: Tree-Based SMT

ACL ID I05-2049
Title Statistical Machine Translation Part II: Tree-Based SMT
Venue International Joint Conference on Natural Language Processing
Session poster-demo-tutorial
Year 2005
  • Dekai Wu (University of Science and Technology, Clear Water Bay Hong Kong)

One of the most active and promising areas of statistical machine translation (SMT) research are tree-based SMT approaches. Tree-based SMT has the potential to overcome the weaknesses of early SMT architectures which (a) do not handle long-distance dependencies well, and (b) are underconstrained in that they allow too much flexibility in word reordering. In this tutorial, we will review the various possible approaches to tree-based SMT, ranging from the original Inversion Transduction Grammar (ITG) models to later models such as alignment templates, dependency models, tree- to-string models, tree-to-tree models, and also probabilistic EBMT models. We will discuss the theoretical relationships between approaches, with critical analysis of their strengths and weaknesses. Within this framewor...