We present an unsupervised approach to symmetric word alignment in which two simple asymmetric models are trained jointly to maximize a combination of data likelihood and agreement between the models. Compared to the standard practice of intersecting predictions of independently-trained models, joint training provides a 32% reduction in AER. Moreover, a simple and efficient pair of HMM aligners provides a 29% reduction in AER over symmetrized IBM model 4 predictions.
REF propose agreement-based learning, which jointly learns probabilities by maximizing a combination of likelihood and agreement between two directional models.
Alignment By Agreement
Human Language Technology Conference And Meeting Of The North American Association For Computational Linguistics
Computer Science