Comparing reordering constraints for SMT using efficient Bleu oracle computation
Markus Dreyer, Keith Hall, Sanjeev P. Khudanpur · 2007
This paper describes a new method to compare reordering constraints for Statistical Machine Translation. We investigate the best possible (oracle) Bleu score achievable under different reordering constraints. Using dynamic programming, we efficiently find a reordering that approximates the highest attainable Bleu score given a reference and a set of reordering constraints. We present an empirical evaluation of popular reordering constraints: local constraints, the IBM constraints, and the Inversion Transduction Grammar (ITG) constraints. We present results for a German-English translation task and show that reordering under the ITG constraints can improve over the baseline by more than 7.5 Bleu points.