We propose a novel language-independent approach for improving statistical machine translation for resource-poor languages by exploiting their similarity to resource-rich ones. More precisely, we improve the translation from a resource-poor source language X1 into a resource-rich language Y given a bi-text containing a limited number of parallel sentences for X1-Y and a larger bi-text for X2-Y for some resource-rich language X2 that is closely related to X1. The evaluation for Indonesian→English (using Malay) and Spanish→English (using Portuguese and pretending Spanish is resource-poor) shows an absolute gain of up to 1.35 and 3.37 Bleu points, respectively, which is an improvement over the rivaling approaches, while using much less additional data.
No takes yet. Share an insight, caveat, or question.
Nakov et al. (2009) studied this question.