Paper: Automatic Acquisition of Hierarchical Transduction Models for Machine Translation

ACL ID P98-1006
Title Automatic Acquisition of Hierarchical Transduction Models for Machine Translation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1998
Authors

We describe a method for the fully automatic learning of hierarchical finite state translation models. The input to the method is transcribed speech utterances and their corresponding hu- man translations, and the output is a set of head transducers, i.e. statistical lexical head- outward transducers. A word-alignment func- tion and a head-ranking function are first ob- tained, and then counts are generated for hy- pothesized state transitions of head transduc- ers whose lexical translations and word order changes are consistent with the alignment. The method has been applied to create an English- Spanish translation model for a speech trans- lation application, with word accuracy of over 75% as measured by a string-distance compari- son to three reference translations.