Inferring Maximally Invertible Bi-grammars for Example-Based Machine Translation
نویسنده
چکیده
This paper discusses inference strategies of context-free bi-grammars for example based machine translation (EBMT). The EBMT system EDGAR is discussed in detail. The notion of invertible context-free feature bi-grammar is introduced in order to provide a means to decide upon the degree of ambiguity of the inferred bi-grammar. It is claimed that a maximally invertible bi-grammar can enhance the precision of the bilingual alignment process, reduce the complexity of the inferred grammar, and uncover inconsistencies in bi-corpora. This paper describes preliminary reeections and thus no empirical evaluation of the method is provided.
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