Date of Original Version
Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Copyright 2013 ACL
Abstract or Description
We present a simple log-linear reparameterization of IBM Model 2 that overcomes problems arising from Model 1’s strong assumptions and Model 2’s overparameterization. Efficient inference, likelihood evaluation, and parameter estimation algorithms are provided. Training the model is consistently ten times faster than Model 4. On three large-scale translation tasks, systems built using our alignment model outperform IBM Model 4.
An open-source implementation of the alignment model described in this paper is available from http://github.com/clab/fast align .
Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 644-649.