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COLING
2002

Study of Practical Effectiveness for Machine Translation Using Recursive Chain-link-type Learning

9 years 2 months ago
Study of Practical Effectiveness for Machine Translation Using Recursive Chain-link-type Learning
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-linktype 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.
Hiroshi Echizen-ya, Kenji Araki, Yoshio Momouchi,
Added 17 Dec 2010
Updated 17 Dec 2010
Type Journal
Year 2002
Where COLING
Authors Hiroshi Echizen-ya, Kenji Araki, Yoshio Momouchi, Koji Tochinai
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