Abstract

We describe a method for automatically learning headtransducer models of translation from examples consisting of transcribed spoken utterances and their translations. The method proceeds by first searching for a hierarchical alignment (specifically a synchronized dependency tree) of each training example. The alignments produced are optimal with respect to a cost function that takes into account co-occurrence statistics and the recursive decomposition of the example into aligned substrings. A probabilistic head-transducer model is then constructed from the alignments. We report results of applying the method to English-to-Spanish translation in the domain of air travel information and Englishto-Japanese translation in the domain of telephone operator assistance. We also report on a variation on this modelconstruction method in which multi-word pairings are used in the computation of the hierarchical alignments and head transducer models. 1.

Citations