A Corpus Level MIRA Tuning Strategy for Machine Translation

Ming Tan, Tian Xia, Shaojun Wang, Bowen Zhou

Abstract: 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-toEnglish translation show its effectiveness over two state-of-the-art MIRA implementations.