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 F-and B scores compared to the interpolation approach of Wang et al. Further experiments reveal that the choice of pivot language does not significantly affect performance.
展开▼
