Improving the Quality of Word Alignment by Integrating Pearson´s Chi-Square Test Information

Previous researches mainly focus on the approaches which are essentially inspirited from the log-linear model background in machine learning or other adaptations. However, not a lot of studies deeply focus on improving word-alignment models to enhance the quality of phrase translation table. This research will follow on that approach. The experiments show that this scheme could also improve the quality of the word-alignment component better. Hence, the improvement impacts the quality of translation system in overall around 1% for the BLEU score metric.