5th International Conference on Spoken Language Processing

Sydney, Australia
November 30 - December 4, 1998

Learning Phrase-Based Head Transduction Models for Translation of Spoken Utterances

Hiyan Alshawi, Srinivas Bangalore, Shona Douglas

AT&T Labs, USA

We describe a method for learning head-transducer models of translation automatically from examples consisting of transcribed spoken utterances and reference translations of the utterances. 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 English-to-Japanese translation in the domain of telephone operator assistance. We also report on a variation on this model-construction method in which multi-word pairings are used in the computation of the hierarchical alignments and head transducer models.

Full Paper

Bibliographic reference.  Alshawi, Hiyan / Bangalore, Srinivas / Douglas, Shona (1998): "Learning phrase-based head transduction models for translation of spoken utterances", In ICSLP-1998, paper 0293.