Improving the Confidence of Machine Translation Quality Estimates
Lucia Specia, Marco Turchi, Zhuoran Wang, John Shawe-Taylor and Craig Saunders
In: Machine Translation Summit XII, 26-30 Aug 2009, Ottawa, Canada.
Summary: We investigate the problem of estimating the quality of the output of machine translation systems at the sentence level when reference translations are not available. The focus is on automatically identifying a threshold to map a continuous predicted score into "good" /"bad" categories for filtering out bad-quality cases in a translation post-edition task. We use the theory of Inductive Confidence Machines (ICM) to identify this threshold according to a confidence level that is expected for a given task. Experiments show that this approach gives improved estimates when compared to those based on classification or regression algorithms without ICM.