Qualitative Analysis of Contemporary Urdu Machine Translation Systems

The diversity in source and target languages coupled with source language ambiguity makes Machine Translation (MT) an exceptionally hard problem. The highly information intensive corpus based MT leads the MT research field today, with Example Based MT and Statistical MT representing two dissimilar frameworks in the data-driven paradigm. Example Based MT is another approach that involves matching of examples from large amount of training data followed by adaptation and re-combination. Urdu MT is still in its infancy due to nominal availability of required data and computational resources. This paper provides a detailed survey of the aforementioned contemporary MT techniques and reports findings based on qualitative analysis with some quantitative BLEU metric quantitative results. Strengths and weaknesses of each technique have been brought to surface through special focus and discussion on examples from Urdu language. The paper concludes with proposal of future directions for research in Urdu machine translation.