Context Adaptation in Statistical Machine Translation Using Models with Exponentially Decaying Cache
نویسنده
چکیده
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.
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