Abstract
We present an Example-Based Machine Translation approach which recursively divides the sentence to be translated, and translates each part separately. The sentence is divided according to the structure of similar examples extracted during the matching process. The approach is especially intended for languages where resources and tools are pretty much unavailable. POS taggers are the only tools utilized, and the bilingual corpus the only resource employed. In addition, the translation system contains an analogy-based sub-sentential alignment module, which predicts word correspondences between new pairs of sentences. This module causes the corpus to grow because new examples can be appended automatically. Consequently, a relatively small initial corpus is sufficient for the translation system to start. The approach has been tested on a French-Japanese corpus of spoken language and produced promising results worthy of further investigation.
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Andriamanankasina, T., Araki, K., Tochinai, K. (2003). EBMT of POS-Tagged Sentences by Recursive Division Via Inductive Learning. In: Carl, M., Way, A. (eds) Recent Advances in Example-Based Machine Translation. Text, Speech and Language Technology, vol 21. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0181-6_8
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DOI: https://doi.org/10.1007/978-94-010-0181-6_8
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