Reputation-Based Selection of Language Services

Quality of Service (QoS) can be used to select desired services from among those offering the equivalent function. In language services such as machine translation, one of the QoS metrics is translation accuracy. However, the problems are that evaluating the translation accuracy is too expensive, that the translation accuracy varies with the difficulty of the task, and that the usefulness of the translation to the user depends on the abilities of the user. In this paper, we propose a framework that selects a useful service for a specific user and task by using reputation information of users, which can be obtained at low cost. First, hypothetical reasoning is used to estimate the partial order relation between the accuracy of the language services, the language ability of the users, and the difficulty of the tasks. Second, deductive reasoning is applied to recommend useful services given the user and the task. We propose a reputation-based language service selection system that combines a partial order acquisition system with a service selection system.