  IBM models are very important word alignment models in Machine Translation.
Following the Maximum Likelihood Estimation principle to estimate their
parameters, the models will easily overfit the training data when the data are
sparse. While smoothing is a very popular solution in Language Model, there
still lacks studies on smoothing for word alignment. In this paper, we propose
a framework which generalizes the notable work Moore [2004] of applying
additive smoothing to word alignment models. The framework allows developers to
customize the smoothing amount for each pair of word. The added amount will be
scaled appropriately by a common factor which reflects how much the framework
trusts the adding strategy according to the performance on data. We also
carefully examine various performance criteria and propose a smoothened version
of the error count, which generally gives the best result.
