Paper: Log-Linear Models For Word Alignment

ACL ID P05-1057
Title Log-Linear Models For Word Alignment
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2005

We present a framework for word align- ment based on log-linear models. All knowledge sources are treated as feature functions, which depend on the source langauge sentence, the target language sentence and possible additional vari- ables. Log-linear models allow statis- tical alignment models to be easily ex- tended by incorporating syntactic infor- mation. In this paper, we use IBM Model 3 alignment probabilities, POS correspon- dence, and bilingual dictionary cover- age as features. Our experiments show that log-linear models significantly out- perform IBM translation models.