Publications

Abstract : This paper presents a corpus-evidence based scheme for deciding whether the translation of an English sentence into Hindi will involve divergence. Divergence is the phenomenon when sentences of similar structure in the source language do not translate into structurally similar sentences in the target language. Divergence assumes special significance in the domain of Example Based Machine Translation (EBMT) where translation of a given sentence is generated by first retrieving translation example(s) of similar sentence(s) from the system's example base, and then by adapting them suitably to meet the requirements of the present input sentence. Surely, occurrence of divergence poses a great hindrance in efficient adaptation of retrieved sentences. A possible remedy may lie in dividing the example base of an EBMT system into two parts: examples of normal translation, in one, and examples involving divergence in the other, so that given an input, the retrieval can be made from the appropriate part of the example base. But success of this scheme depends heavily on the system's ability to judge a priori whether translation of a given input will involve divergence. The task, however, is not straightforward as occurrence of divergence does not follow any rules that make their prior identification simple. The technique proposed here is aimed at achieving this goal. The scheme is explained and illustrated in the context of English to Hindi EBMT.