Online adaptation strategies for statistical machine translation in post-editing scenarios

Authors:
Pascual MartíNez-GóMez;GermáN Sanchis-Trilles;Francisco Casacuberta
Affiliations:
Graduate School of Information Science and Technology, Department of Computer Science, The University of Tokyo, Japan;Departamento de Sistemas Informáticos y Computación, Instituto Tecnológico de Informática, Universitat Politècnica de València, Spain;Departamento de Sistemas Informáticos y Computación, Instituto Tecnológico de Informática, Universitat Politècnica de València, Spain
Venue:
Pattern Recognition
Year:
2012

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Abstract

One of the most promising approaches to machine translation consists in formulating the problem by means of a pattern recognition approach. By doing so, there are some tasks in which online adaptation is needed in order to adapt the system to changing scenarios. In the present work, we perform an exhaustive comparison of four online learning algorithms when combined with two adaptation strategies for the task of online adaptation in statistical machine translation. Two of these algorithms are already well-known in the pattern recognition community, such as the perceptron and passive-aggressive algorithms, but here they are thoroughly analyzed for their applicability in the statistical machine translation task. In addition, we also compare them with two novel methods, i.e., Bayesian predictive adaptation and discriminative ridge regression. In statistical machine translation, the most successful approach is based on a log-linear approximation to a posteriori distribution. According to experimental results, adapting the scaling factors of this log-linear combination of models using discriminative ridge regression or Bayesian predictive adaptation yields the best performance.