IBM models are very popular word alignment models in Machine Translation. They play critical roles in the systems of this field. These models follow Maximum Likelihood principle to estimate their parameters. However, in many case, the models will be too fit the training data that may result in wrong word alignments on testing data. Smoothing is a popular solution to the overfitting problem when the causes are rare events. While this technique is very common in Language Model which is another problem in Machine Translation, there is still lack of studies for the problem of word alignment. \cite{moore2004improving} reported a study on a simple method of additive smoothing, in which the amount to add is learnt from annotated data. This basic technique gives a significant improvement over the unsmoothed version. With such a good motivation, in this paper, we propose a more general framework by varying the amount to add rather than adding only a constant amount as the original additive smoothing. In term of learning method, we also experience a method to learn the parameter of smoothing from unannotated data with a deep analysis and comparision between different learning methods.
['Vuong Van Bui', 'Cuong Anh Le']
Smoothing the parameter of IBM word alignment models: the framework and its learning approaches