Study of practical effectiveness for machine translation using recursive chain-link-type learning

Authors:
Hiroshi Echizen-ya;Yoshio Momouchi;Kenji Araki;Koji Tochinai
Affiliations:
Hokkai-Gakuen University, Chuo-ku, Sapporo, Japan;Hokkai-Gakuen University, Chuo-ku, Sapporo, Japan;Hokkaido University, Kita-ku, Sapporo, Japan;Hokkai-Gakuen University, Asahi-machi, Toyohira-ku, Sapporo, Japan
Venue:
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Year:
2002

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Abstract

A number of machine translation systems based on the learning algorithms are presented. These methods acquire translation rules from pairs of similar sentences in a bilingual text corpora. This means that it is difficult for the systems to acquire the translation rules from sparse data. As a result, these methods require large amounts of training data in order to acquire high-quality translation rules. To overcome this problem, we propose a method of machine translation using a Recursive Chain-link-type Learning. In our new method, the system can acquire many new high-quality translation rules from sparse translation examples based on already acquired translation rules. Therefore, acquisition of new translation rules results in the generation of more new translation rules. Such a process of acquisition of translation rules is like a linked chain. From the results of evaluation experiments, we confirmed the effectiveness of Recursive Chain-link-type Learning.