The lack of parallel data for many language pairs is an important challenge to statistical machine translation (SMT). One common solution is to pivot through a third language for which there exist parallel corpora with the source and target languages. Although pivoting is a robust technique, it introduces some low quality translations especially when a poor morphology language is used as the pivot between rich morphology languages. In this paper, we examine the use of synchronous morphology constraint features to improve the quality of phrase pivot SMT. We compare hand-crafted constraints to those learned from limited parallel data between source and target languages. The learned morphology constraints are based on projected align- ments between the source and target phrases in the pivot phrase table. We show positive results on Hebrew-Arabic SMT (pivoting on English). We get 1.5 BLEU points over a phrase pivot baseline and 0.8 BLEU points over a system combination baseline with a direct model built from parallel data.

本文研究使用同步形态约束特征来提高短语枢轴机器翻译的质量，通过将源语言和目标语言的语段在枢轴表中的对应关系作为基础，建立手工制作的约束条件和利用有限的平行数据学习的约束条件，并在英语作为枢轴的希伯来语-阿拉伯语翻译中表现出积极的结果，相较于短语枢轴和系统组合基线模型，我们的模型建立在平行数据上得到了 0.8 BLEU 点的增量，基于短语枢轴模型得到了 1.5 BLEU 点的增量。

基于短语核心的统计机器翻译的形态学约束