Paper: Machine Translation Using Probabilistic Synchronous Dependency Insertion Grammars

ACL ID P05-1067
Title Machine Translation Using Probabilistic Synchronous Dependency Insertion Grammars
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2005
Authors

Syntax-based statistical machine transla- tion (MT) aims at applying statistical models to structured data. In this paper, we present a syntax-based statistical ma- chine translation system based on a prob- abilistic synchronous dependency insertion grammar. Synchronous depend- ency insertion grammars are a version of synchronous grammars defined on de- pendency trees. We first introduce our approach to inducing such a grammar from parallel corpora. Second, we de- scribe the graphical model for the ma- chine translation task, which can also be viewed as a stochastic tree-to-tree trans- ducer. We introduce a polynomial time decoding algorithm for the model. We evaluate the outputs of our MT system us- ing the NIST and Bleu automatic MT evaluation software. The result shows that our system o...