Departmental Papers (CIS)
Title
Document Type
Conference Paper
Date of this Version
6-2006
Abstract
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.
Date Posted: 16 July 2012
Comments
Percy Liang, Ben Taskar, and Dan Klein. 2006. Alignment by agreement. In Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics (HLT-NAACL '06). Association for Computational Linguistics, Stroudsburg, PA, USA, 104-111. DOI=10.3115/1220835.1220849 http://dx.doi.org/10.3115/1220835.1220849
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