Discriminative Weighted Alignment Matrices For Statistical Machine Translation

In extant phrase-based statistical machine translation (SMT) systems, the transla-tion model relies on word-to-word align-ments, which serve as constraints for the subsequent heuristic extraction and scor-ing processes. Word alignments are usu-ally inferred in a probabilistic framework; yet, only one single best alignment is re-tained, as if alignments were deterministi-cally produced. In this paper, we explore ways to take into account the entire align-ment matrix, where each alignment link is scored by its probability. By compari-son with previous attempts, we use an ex-ponential model to compute these proba-bilities, which enables us to achieve sig-nificant improvements on the NIST MT’09 Arabic-English translation task.