A Corpus Level MIRA Tuning Strategy for Machine Translation
Ming Tan, Tian Xia, Shaojun Wang, Bowen Zhou · 2013
MIRA based tuning methods have been widely used in statistical machine translation (SMT) system with a large number of features.Since the corpus-level BLEU is not decomposable, these MIRA approaches usually define a variety of heuristic-driven sentencelevel BLEUs in their model losses.Instead, we present a new MIRA method, which employs an exact corpus-level BLEU to compute the model loss.Our method is simpler in implementation.Experiments on Chinese-to-English translation show its effectiveness over two state-of-the-art MIRA implementations.