Incorporating machine learning techniques in MT evaluation

Abstract

From a project manager’s perspective, Machine Translation (MT) Evaluation is the most important activity in MT development. Using the results produced through MT Evaluation, one can assess the progress of MT development task. Traditionally, MT Evaluation is done either by human experts who have the knowledge of both source and target languages or it is done by automatic evaluation metrics. These both techniques have their pros and cons. Human evaluation is very time consuming and expensive but at the same time it provides good and accurate status of MT Engines. Automatic evaluation metrics on the other hand provides very fast results but lacks the precision provided by human judges. Thus a need is being felt for a mechanism which can produce fast results along with a good correlation with the results produced by human evaluation. In this paper, we have addressed this issue where we would be showing the implementation of machine learning techniques in MT Evaluation. Further, we would also compare the results of this evaluation with human and automatic evaluation.