Consistency-Aware Search for Word Alignment

Abstract As conventional word alignment search algorithms usually ignore the consistency constraint in translation rule extraction, improving alignment accuracy does not necessarily increase translation quality. We propose to use coverage, which reflects how well extracted phrases can recover the training data, to enable word alignment to model consistency and correlate better with machine translation. This can be done by introducing an objective that maximizes both alignment model score and coverage. We introduce an efficient algorithm to calculate coverage on the fly during search. Experiments show that our consistency-aware search algorithm significantly outperforms both generative and discriminative alignment approaches across various languages and translation models

Consistency-Aware Search for Word Alignment

As conventional word alignment search algorithms usually ignore the consistency constraint in translation rule extraction, improving alignment accuracy does not necessarily increase translation quality. We propose to use coverage, which reflects how well extracted phrases can recover the training data, to enable word alignment to model consistency and corre-late better with machine translation. This can be done by introducing an objective that maximizes both alignment model score and coverage. We introduce an efficient algorithm to calculate coverage on the fly during search. Experiments show that our consistency-aware search algorithm significantly outperforms both generative and discriminative alignment approaches across various languages and translation models.