State-of-the-art machine translation techniques are still far from producing high quality translations. This drawback leads us to introduce an alternative approach to the translation problem that brings human expertise into the machine translation scenario. In this framework, namely Computer Assisted Translation (CAT), human translators interact with a translation system, as an assistance tool, that dinamically offers, a list of translations that best completes the part of the sentence already translated. In this paper, finite state transducers are presented as a candidate technology in the CAT paradigm. The appropriateness of this technique is evaluated on a printer manual corpus and results from preliminary experiments confirm that human translators would reduce to less than 25% the amount of work to be done for the same task.
In REF finite state transducers are presented as a candidate technology in the CAT paradigm.
From Machine Translation To Computer Assisted Translation Using Finite-State Models
SIGDAT Conference On Empirical Methods In Natural Language Processing
Computer Science