Analyzing Optimization for Statistical Machine Translation: MERT Learns Verbosity, PRO Learns Length
Francisco Guzmán, Preslav Nakov, Stephan Vogel · 2015
We study the impact of source length and verbosity of the tuning dataset on the performance of parameter optimizers such as MERT and PRO for statistical machine translation.In particular, we test whether the verbosity of the resulting translations can be modified by varying the length or the verbosity of the tuning sentences.We find that MERT learns the tuning set verbosity very well, while PRO is sensitive to both the verbosity and the length of the source sentences in the tuning set; yet, overall PRO learns best from highverbosity tuning datasets.