Lexical-semantic divergence in Urdu-to-English Example Based Machine Translation

Divergence is one of the common obstacles in Example Based Machine Translation (EBMT). It needs to be identified, categorized and resolved to obtain correct translation for any pair of language. The focus in this research is on lexical semantic divergence and six different types are identified and generalizations are made on the basis of examples, for Urdu to English translation. Strategies are also presented for the identification of these types. This work helps in efficient retrieval of the examples from a bilingual corpus, which in turn will help in the alignment and recombination stages of EBMT, to produce good quality translation.