BiTAM

Abstract

We propose a novel bilingual topical admixture (BiTAM) formalism for word alignment in statistical machine translation. Under this formalism, the parallel sentence-pairs within a document-pair are assumed to constitute a mixture of hidden topics; each word-pair follows a topic-specific bilingual translation model. Three BiTAM models are proposed to capture topic sharing at different levels of linguistic granularity (i.e., at the sentence or word levels). These models enable wordalignment process to leverage topical contents of document-pairs. Efficient variational approximation algorithms are designed for inference and parameter estimation. With the inferred latent topics, BiTAM models facilitate coherent pairing of bilingual linguistic entities that share common topical aspects. Our preliminary experiments show that the proposed models improve word alignment accuracy, and lead to better translation quality.