A Corpus Level MIRA Tuning Strategy for Machine Translation
نویسندگان
چکیده
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.
منابع مشابه
Search-Aware Tuning for Machine Translation
Parameter tuning is an important problem in statistical machine translation, but surprisingly, most existing methods such as MERT, MIRA and PRO are agnostic about search, while search errors could severely degrade translation quality. We propose a searchaware framework to promote promising partial translations, preventing them from being pruned. To do so we develop two metrics to evaluate parti...
متن کاملBatch Tuning Strategies for Statistical Machine Translation
There has been a proliferation of recent work on SMT tuning algorithms capable of handling larger feature sets than the traditional MERT approach. We analyze a number of these algorithms in terms of their sentencelevel loss functions, which motivates several new approaches, including a Structured SVM. We perform empirical comparisons of eight different tuning strategies, including MERT, in a va...
متن کاملSearch-Aware Tuning for Hierarchical Phrase-based Decoding
Parameter tuning is a key problem for statistical machine translation (SMT). Most popular parameter tuning algorithms for SMT are agnostic of decoding, resulting in parameters vulnerable to search errors in decoding. The recent research of “search-aware tuning” (Liu and Huang, 2014) addresses this problem by considering the partial derivations in every decoding step so that the promising ones a...
متن کاملAPRO: All-Pairs Ranking Optimization for MT Tuning
We present APRO, a new method for machine translation tuning that can handle large feature sets. As opposed to other popular methods (e.g., MERT, MIRA, PRO), which involve randomness and require multiple runs to obtain a reliable result, APRO gives the same result on any run, given initial feature weights. APRO follows the pairwise ranking approach of PRO (Hopkins and May, 2011), but instead of...
متن کاملTuning as Ranking
We offer a simple, effective, and scalable method for statistical machine translation parameter tuning based on the pairwise approach to ranking (Herbrich et al., 1999). Unlike the popular MERT algorithm (Och, 2003), our pairwise ranking optimization (PRO) method is not limited to a handful of parameters and can easily handle systems with thousands of features. Moreover, unlike recent approache...
متن کامل