We describe a class of translation model in which a set of input variants encoded as a context-free forest is translated using a finitestate translation model. The forest structure of the input is well-suited to representing word order alternatives, making it straightforward to model translation as a two step process: (1) tree-based source reordering and (2) phrase transduction. By treating the reordering process as a latent variable in a probabilistic translation model, we can learn a long-range source reordering model without example reordered sentences, which are problematic to construct. The resulting model has state-of-the-art translation performance, uses linguistically motivated features to effectively model long range reordering, and is significantly smaller than a comparable hierarchical phrase-based translation model.
REF use an input forest structure to represent word-order alternatives and learn models for long-range source reordering that maximize translation quality.
Context-free reordering, finite-state translation
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Computer Science