Abstract
This paper proposes a learning mechanism to acquire structural correspondences between two languages from a corpus of translated sentence pairs. The proposed mechanism uses analogical reasoning between two translations. Given a pair of translations, the similar parts of the sentences in the source language must correspond the similar parts of the sentences in the target language. Similarly, the different parts should correspond to the respective parts in the translated sentences. The correspondences between the similarities, and also differences are learned in the form of rewrite rules. The system is tested on a small training dataset and produced promising results for further investigation.
This research has been supported in part by NATO Science for Stability Program Grant TU-LANGUAGE.
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© 1996 Springer-Verlag Berlin Heidelberg
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Güvenir, H.A., Tunç, A. (1996). Corpus-based learning of generalized parse tree rules for translation. In: McCalla, G. (eds) Advances in Artifical Intelligence. Canadian AI 1996. Lecture Notes in Computer Science, vol 1081. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61291-2_46
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DOI: https://doi.org/10.1007/3-540-61291-2_46
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