Hierarchical Incremental Adaptation for Statistical Machine Translation
Joern Wuebker, Spence Green, John DeNero · 2015
We present an incremental adaptation approach for statistical machine translation that maintains a flexible hierarchical domain structure within a single consistent model.Both weights and rules are updated incrementally on a stream of post-edits.Our multi-level domain hierarchy allows the system to adapt simultaneously towards local context at different levels of granularity, including genres and individual documents.Our experiments show consistent improvements in translation quality from all components of our approach.