Multi-Task Word Alignment Triangulation for Low-Resource Languages

We present a multi-task learning approach that jointly trains three word alignment models over disjoint bitexts of three languages: source, target and pivot. Our approach builds upon model triangulation, following Wang et al., which approximates a source-target model by combining source-pivot and pivot-target models. We develop a MAP-EM algorithm that uses triangulation as a prior, and show how to extend it to a multi-task setting. On a low-resource Czech-English corpus, using French as the pivot, our multi-task learning approach more than doubles the gains in both Fand Bleu scores compared to the interpolation approach of Wang et al. Further experiments reveal that the choice of pivot language does not significantly affect performance.