Example-based machine translation using DP-matching between work sequences

E. Sumita

Published 2001 in DDMMT@ACL

ABSTRACT

We propose a new approach under the example-based machine translation paradigm. First, the proposed approach retrieves the most similar example by carrying out DP-matching of the input sentence and example sentences while measuring the semantic distance of the words. Second, the approach adjusts the gap between the input and the most similar example by using a bilingual dictionary. We show the results of a computational experiment.

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