Using Rich Linguistic and Contextual Information for Tree-Based Statistical Machine Translation

Abstract

This paper presents an approach to select appropriate translation rules to improve phrase-reordering of tree-based statistical machine translation. We propose new features with rich linguistic and contextual information. We give a new algorithm to extract features, use maximum entropy to combine rich linguistic and contextual information and integrate these features into the tree-based SMT model (Moses-chart). We obtain substantial improvements in performance for tree-based translation from Vietnamese to English.