Corpus-based learning of generalized parse tree rules for translation

摘要

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 tanslations. 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 corespondences between the similarities, and also differences are learned in the from of rewrite rules. The system is tested on a small training dataset and produced promising results for further investigation.