A Convex Alternative to IBM Model 2

Andrei Simion, Michael Collins, Cliff Stein

Abstract: The IBM translation models have been hugely influential in statistical machine translation; they are the basis of the alignment models used in modern translation systems. Excluding IBM Model 1, the IBM translation models, and practically all variants proposed in the literature, have relied on the optimization of likelihood functions or similar functions that are non-convex, and hence have multiple local optima. In this paper we introduce a convex relaxation of IBM Model 2, and describe an optimization algorithm for the relaxation based on a subgradient method combined with exponentiated-gradient updates. Our approach gives the same level of alignment accuracy as IBM Model 2.