Online Large-Margin Training for Statistical Machine Translation
Taro Watanabe, Jun Suzuki, Hajime Tsukada, Hideki Isozaki · 2007
We achieved a state of the art performance in statistical machine translation by using a large number of features with an online large-margin training algorithm. The millions of parameters were tuned only on a small development set consisting of less than 1K sentences. Experiments on Arabic-to-English translation indicated that a model trained with sparse binary features outperformed a conventional SMT system with a small number of features. 1