Maximum likelihood and discriminative training of direct translation models

We consider translating natural language sentences into a formal language using direct translation models built automatically from training data. Direct translation models have three components: an arbitrary prior conditional probability distribution, features that capture correlations between automatically determined key phrases or sets of words in both languages, and weights associated with these features. The features and the weights are selected using a training corpus of matched pairs of source and target language sentences to maximize the entropy or a new discrimination measure of the resulting conditional probability model. We report results in the air travel information system domain and compare the two methods of training.