APRO: 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 ranking a small sampled subset of pairs from the kbest list, APRO efficiently ranks all pairs. By obviating the need for manually determined sampling settings, we obtain more reliable results. APRO converges more quickly than PRO and gives similar or better translation results.

Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics (ACL): Human Language Technologies