Paper: Cooperation Between Transfer And Analysis In Example-Based Framework

ACL ID C92-2097
Title Cooperation Between Transfer And Analysis In Example-Based Framework
Venue International Conference on Computational Linguistics
Session Main Conference
Year 1992
Authors

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 transfer and analysis knowledge. A consistent framework of examples makes the cooperation between transfer and analysis effective, and efficient translation is achieved. The TDMT prototype system, which translates Japanese spoken dialogs into English, has shown great promise.