Statistical machine translation (SMT) treats the translation of natural language as a machine
learning problem. By examining many samples of human-produced translation, SMT …

The Handbook of Natural Language Processing, Second Edition presents practical tools
and techniques for implementing natural language processing in computer systems. Along …

Efficient decoding has been a fundamental problem in machine translation, especially with
an integrated language model which is essential for achieving good translation quality. We …

Machine translation can be considered a highly interdisciplinary and multidisciplinary field
because it is approached from the point of view of human translators, engineers, computer …

This work investigates supervised word alignment methods that exploit inversion
transduction grammar (ITG) constraints. We consider maximum margin and conditional …

We investigate the task of unsupervised constituency parsing from bilingual parallel corpora.
Our goal is to use bilingual cues to learn improved parsing models for each language and to …

We combine the strengths of Bayesian modeling and synchronous grammar in
unsupervised learning of basic translation phrase pairs. The structured space of a …

We present a new implication of Wu's (1997) Inversion Transduction Grammar (ITG)
Hypothesis, on the problem of retrieving truly parallel sentence translations from large …

We present a phrasal inversion transduction grammar as an alternative to joint phrasal
translation models. This syntactic model is similar to its flatstring phrasal predecessors, but …

In this paper, we present a Bayesian Learning based method to train word dependent
transition models for HMM based word alignment. We present word alignment results on the …