Discriminative Reordering Models for Statistical Machine Translation
Abstract
We present discriminative reordering models for phrase-based statistical machine translation. The models are trained
using the maximum entropy principle. We use several types of features: based on words, based on word classes, based on
the local context. We evaluate the overall performance of the reordering models as well as the contribution of the individual feature types on a word-aligned corpus.
Additionally, we show improved translation performance using these reordering models compared to a state-of-the-art
baseline system.