Fast decoding for statistical machine translation
Ye‐Yi Wang, Alex Waibel · 1998
We investigated an efficient decoding algorithm for statistical machine translation. Compared to the other algorithms, this new algorithm is applicable to different translation models, and it is much faster. Experiments showed that the algorithm achieved an overall performance comparable to the state of the art decoding algorithms. 1. INTRODUCTION A statistical machine translation system consists of three sub-tasks: the modeling task describes machine translation processes with stochastic models; the learning task estimates the parameters in the models; and the decoding task searches for the translation that has the highest score according to the models. [1, 2, 3] described different translation models and their learning algorithms. [4, 2, 5, 6] introduced different decoding algorithms. However, those decoding algorithms have many limitations. Below is a brief review of these algorithms: 1.1. IBM Stack Decoder In the IBM Stack Decoder [4], a hypothesis is comprised of a source sent...