Paper: Automatic Acquisition of Hierarchical Transduction Models for Machine Translation

ACL ID C98-1006
Title Automatic Acquisition of Hierarchical Transduction Models for Machine Translation
Venue International Conference on Computational Linguistics
Session Main Conference
Year 1998
Authors

Ve describe a method for the fully automatic learning of hierarchical Iinite state translation models. The input to lhe method is transcribed sl)eech utterances and th(>ir corre.~l)on(ling hu. man translations, and the output is a set of head transducers, i.e. statistical le×ical head- outward transducers. A word-alignment func- tion and a head-ranking funcl, ion are [irst ob- tained, and then counts are generated for hy- pothesized stale transitions of head transduc- ers whose lexical translations and word order chauges are consislent with the alignment. The method has been applied to create au English- Spanish translation model for ~t speech tI'ans- lation application, with word accuracy of over 75% a,s measured by a string-dislauce compari- son to three reference tra.n...