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.
Liang et al. REF propose a symmetric alignment, which trains two asymmetric models jointly to maximize agreement between the models.
Alignment By Agreement
Human Language Technology Conference And Meeting Of The North American Association For Computational Linguistics
Computer Science