Search-Aware Tuning for Machine Translation

Parameter tuning is an important problem in statistical machine translation, but surpris-ingly, most existing methods such as MERT, MIRA and PRO are agnostic about search, while search errors could severely degrade translation quality. We propose a search-aware framework to promote promising par-tial translations, preventing them from be-ing pruned. To do so we develop two met-rics to evaluate partial derivations. Our tech-nique can be applied to all of the three above-mentioned tuning methods, and ex-tensive experiments on Chinese-to-English and English-to-Chinese translation show up to +2.6 BLEU gains over search-agnostic baselines.