Log-Linear Models for Word Alignment

Yang Liu, Qun Liu, Shouxun Lin

Abstract: We present a framework for word alignment based on log-linear models. All knowledge sources are treated as feature functions, which depend on the source language sentence, the target language sentence and possible additional variables. Log-linear models allow statistical alignment models to be easily extended by incorporating syntactic information. In this paper, we use IBM Model 3 alignment probabilities, POS correspondence, and bilingual dictionary coverage as features. Our experiments show that log-linear models significantly outperform IBM translation models.