Publication

Abstract : Divergence is a key aspect of translation between two languages. Divergence occurs when structurally similar sentences of the source language do not translate into sentences that are similar in structures in the target language. Divergence assumes special significance in the domain of Example-Based Machine Translation (EBMT). An EBMT system generates translation of a given sentence by retrieving similar past translation examples from its example base and then adapting them suitably to meet the current translation requirements. Divergence imposes a great challenge to the success of EBMT. The present work provides a technique for identification of divergence without going into the semantic details of the underlying sentences. This identification helps in partitioning the example database into divergence / non-divergence categories, which in turn should facilitate efficient retrieval and adaptation in an EBMT system