An increased number of machine translation services are now available. Unfortunately, none of them can provide adequate translation quality for all input sources. This forces the user to select from among the services according to his needs. However, it is tedious and time consuming to perform this manual selection. Our solution, proposed here, is an automatic mechanism that can select the most appropriate machine translation service. Although evaluation methods are available, such as BLEU, NIST, WER, etc., their evaluation results are not unanimous regardless of the translation sources. We proposed a two-phase architecture for selecting translation services. The first phase uses a data-driven classification to allow the most appropriate evaluation method to be selected according to each translation source. The second phase selects the most appropriate machine translation result by the selected evaluation method. We describe the architecture, detail the algorithm, and construct a prototype. Tests show that the proposal yields better translation quality than employing just one machine translation service.

Due to instant availability and low cost, machine translation is becoming popular. Machine translation mediated communication plays a more and more important role in international collaboration. However, machine translators cannot guarantee high quality translation. In a multilingual communication task, many in-domain resources, for example domain dictionaries, are needed to promote translation quality. This raises the problem of how to help communication task designers provide higher quality translation systems, systems that can take advantage of various in-domain resources. The Language Grid, a service-oriented collective intelligent platform, allows in-domain resources to be wrapped into language services. For task-oriented translation, we propose service composition scenarios for the composition of different language services, where various in-domain resources are utilized effectively. We design the architecture, provide a script language as the interface for the task designer, which is easy for describing the composition scenario, and make a case study of a Japanese-English campus orientation task. Based on the case study, we analyze the increase in translation quality possible and the usage of in-domain resources. The results demonstrate a clear improvement in translation accuracy when the in-domain resources are used.