Large-Scale Statistical Machine Translation with Weighted Finite State Transducers

statistical machine translation system follows a generative model of translation and is implemented by the composition of component models of translation and movement realised as Weighted Finite State Transducers. Our flexible architecture requires no special purpose decoder and readily handles the large-scale natural language processing demands of state-of-the-art machine translation systems. In this paper we describe the CUED system’s participation in the NIST 2008 Arabic-English machine translation evaluation task. Key words: Statistical machine translation, weighted finite state transducers, large-scale natural language processing, finite state grammars.

Exact decoding of phrase-based translation models through Lagrangian relaxation

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 69-72).This thesis describes two algorithms for exact decoding of phrase-based translation models, based on Lagrangian relaxation. Both methods recovers exact solutions, with certificates of optimality, on over 99% of test examples. The first method is much more efficient than approaches based on linear programming (LP) or integer linear programming (ILP) solvers: these methods are not feasible for anything other than short sentences. We compare our methods to MOSES [6], and give precise estimates of the number and magnitude of search errors that MOSES makes.by Yin-Wen Chang.S.M