Learn More
This paper i)roposes an effective parsing nicthod for examlile-based machine transhltiOl~. In this method, an input string is parsed by the tOl)-down aplflication of linguistic patterns consisting ol variables and constituent boundaries. A constituent boundary is expressed by either a functional word or a l)art-of..speech bigram. When structural ambiguity(More)
We h a v e built a new speech translation system called ATR-MATRIX (ATR's Multilingual Automatic Translation System for Information Exchange). This system can recognize natural Japanese utterances such as those used in daily life, translate them into English and output synthesized speech. This system is running on a workstation or a high-end PC and achieves(More)
We have proposed an incremental translation method in Transfer-Driven Machine Translation (TDMT). In this method, constituent boundary patterns are applied to an input in a bottom-up fashion. Also, by dealing with best-only substructures, the explosion of structural ambiguity is constrained and an efficient translation of a lengthy input can be achieved.(More)
Transfer-Driven Machine Translation (TDMT) is presented as a method which drives the translation processes according to the nature of the input. In TDMT, transfer knowledge is the central knowledge of translation, and various kinds aml levels of knowledge are cooperatively applied to input sentences. TDMT effectively utilizes an example-based framework for(More)
This paper describes a practical method of automatic simultaneous interpretation utilizing an example-based incremental transfer mechanism. We primarily show how incremental translation is achieved in the context of an example-based framework. We then examine the type of translation examples required for a simultaneous interpretation to create naturally(More)
Spoken language translation requires both (1) high accuracy and (2) a real-time response which are difficult to achieve using conventional technologies. To fulfill the first requirement, we have adopted an Example-Based Approach. It generates a target sentence by combining partial translations obtained by mimicking best-match partial translation examples.(More)