Reordering Models for Statistical Machine Translation: A Literature Survey

In this survey, we briefly study various reordering models that are used with statistical translation models. Reordering model is one of the important component of any statistical machine translation system. Problem of reordering is NP-Hard itself. In this survey, we study various reordering approaches that can be used to solve this problem. We first study simple distortion-based reordering which is used with phrasebased and factor-based models. Next, we discuss limitations of this distance-based approach. Then we introduce a new source-reordering based approach to handle the reorderings based on structural information of the input text. We study how to use parse trees and shallow parsing for source-side reordering.