From Machine Translation to Computer Assisted Translation using Finite-State Models

From Machine Translation to Computer Assisted Translation using Finite-State Models Jorge Civera, Elsa Cubel, Antonio L. Lagarda, David Pic´o, Jorge Gonz´alez, Enrique Vidal, Francisco Casacuberta Instituto Tecnol´ogico de Inform´atica Dpto. de Sistemas Inform´aticos y Computaci´on, Universidad Polit´ecnica de Valencia E-46071 Valencia, Spain jorcisai@iti.upv.es Juan M. Vilar, Sergio Barrachina Dpto. de Lenguajes y Sistemas Inform´aticos, Universidad Jaime I, E-12071 Castell´on de la Plana, Spain jvilar@lsi.uji.es Abstract State-of-the-art machine translation techniques are still far from producing high quality translations. This drawback leads us to introduce an alterna- tive approach to the translation problem that brings human expertise into the machine translation sce- nario. In this framework, namely Computer As- sisted Translation (CAT), human translators inter- act with a translation system, as an assistance tool, that dinamically offers, a list of translations that best completes the part of the sentence already trans- lated. 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 re- sults from preliminary experiments confirm that hu- man translators would reduce to less than 25% the amount of work to be done for the same task. 1 Introduction 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 sce- nario. (Langlais et al., 2000) proposed this idea that can be illustrated as follows. Initially, the human translator is provided with a possible translation for the sentence to be translated. Unfortunately in most of the cases, this translation is not perfect, so the translator amends it and asks for a translation of the part of the sentence still to be translated (completion). This latter interaction is repeated as many times as needed until the final translation is achieved. The scenario described in the previous para- graph, can be seen as an iterative refinement of the translations offered by the translation system, that without possessing the desired quality, help the translator to increase his/her productivity. Nowa- days, this lack of translation excellence is a common characteristic in all machine translation systems. Therefore, the human-machine synergy represented by the CAT paradigm seems to be more promising than fully-automatic translation in the near future. The CAT paradigm has two important as- pects: the models need to provide adequate com- pletions and they have to do so efficiently to per- form under usability constrains. To fulfill these two requirements, Stochastic Finite State Transducers (SFST) have been selected since they have proved in the past to be able to provide adequate transla- tions (Vidal, 1997; Knight and Al-Onaizan, 1998; Amengual et al., 2000; Casacuberta et al., 2001; Bangalore and Ricardi, 2001). In addition, efficient parsing algorithms can be easily adapted in order to provide completions. The rest of the paper is structured as follows. The following section introduces the general setting for machine translation and finite state models. In section 3, the search …

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