Parameter Optimization for Statistical Machine Translation: It Pays to Learn from Hard Examples
Preslav Nakov, Fahad Al Obaidli, Francisco Guzmán, Stephan Vogel · 2013
Research on statistical machine transla-tion has focused on particular translation directions, typically with English as the target language, e.g., from Arabic to En-glish. When we reverse the translation di-rection, the multiple reference translations turn into multiple possible inputs, which offers both challenges and opportunities. We propose and evaluate several strategies for making use of these multiple inputs: (a) select one of the datasets, (b) select the best input for each sentence, and (c) syn-thesize an input for each sentence by fus-ing the available inputs. Surprisingly, we find out that it is best to tune on the hardest available input, not on the one that yields the highest BLEU score. This finding has implications on how to pick good transla-tors and how to select useful data for pa-rameter optimization in SMT. 1