We present an adaptation technique for statistical machine translation, which applies the well-known Bayesian learning paradigm for adapting the model parameters. Since state-of-t...
In this paper, with a belief that a language model that embraces a larger context provides better prediction ability, we present two extensions to standard n-gram language models ...
We propose a novel language-independent approach for improving statistical machine translation for resource-poor languages by exploiting their similarity to resource-rich ones. Mo...
We describe Akamon, an open source toolkit for tree and forest-based statistical machine translation (Liu et al., 2006; Mi et al., 2008; Mi and Huang, 2008). Akamon implements all...
In statistical machine translation, a researcher seeks to determine whether some innovation (e.g., a new feature, model, or inference algorithm) improves translation quality in co...
Jonathan H. Clark, Chris Dyer, Alon Lavie, Noah A....