Paper: Maximum Entropy Model Learning of the Translation Rules

ACL ID P98-2191
Title Maximum Entropy Model Learning of the Translation Rules
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1998
Authors

This paper proposes a learning method of translation rules from parallel corpora. This method applies the maximum entropy prin- ciple to a probabilistic model of translation rules. First, we define feature functions which express statistical properties of this model. Next, in order to optimize the model, the system iterates following steps: (1) se- lects a feature function which maximizes log- likelihood, and (2) adds this function to the model incrementally. As computational cost associated with this model is too expensive, we propose several methods to suppress the overhead in order to realize the system. The result shows that it attained 69.54% recall rate.