Hierarchical Incremental Adaptation for Statistical Machine Translation

摘要

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 incrementallyonastreamofpost-edits. Our multi-level domain hierarchy allows the system to adapt simultaneously towards local context at dierent levels of granularity, including genres and individual documents. Our experiments show consistent improvements in translation quality from all components of our approach.