| Background | where Pr(e| f) is the probability that e is the translation of the given source string f. To model the posterior probability Pr(e| f) , most of the state-of-the-art SMT systems utilize the log-linear model proposed by Och and Ney (2002), as follows, |
| Background | In this paper, u denotes a log-linear model that has Mfixed features {h1(f,e), ..., hM(f,e)}, ,1 = {3.1, ..., AM} denotes the M parameters of u, and u(/1) denotes a SMT system based on u with parameters ,1. |
| Background | In this paper, we use the term training set to emphasize the training of log-linear model . |