We report results from a domain adaptation task for statistical machine translation (SMT) using cache-based adaptive language and translation models. We apply an exponential decay factor and integrate the cache models in a standard phrasebased SMT decoder. Without the need for any domain-specific resources we obtain a 2.6% relative improvement on average in BLEU scores using our dynamic adaptation procedure.
REF propose cache-based language and translation models, which are built on recently translated sentences.
Context Adaptation in Statistical Machine Translation Using Models with Exponentially Decaying Cache
Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language Processing
Computer Science