An improvement in the selection process of machine translation using inductive learning with genetic algorithms

Authors:
Hiroshi Echizen-ya;Kenji Araki;Yoshikazu Miyanaga;Koji Tochinai
Affiliations:
Hokkaido University, Sapporo, Japan;Hokkai-Gakuen University, Sapporo, Japan;Hokkaido University, Sapporo, Japan;Hokkaido University, Sapporo, Japan
Venue:
ANLC '97 Proceedings of the fifth conference on Applied natural language processing: Descriptions of system demonstrations and videos
Year:
1997

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Abstract

We proposed a method of machine translation using inductive learning with genetic algorithms, and confirmed the effectiveness of applying genetic algorithms. However, the system based on this method produces many erroneous translation rules that cannot be completely removed from the dictionary. Therefore, we need to improve how to apply genetic algorithms to be able to remove erroneous translation rules from the dictionary. In this paper, we describe this improvement in the selection process and the results of evaluation experiments.