Quality Estimation of Machine Translated Texts based on Direct Evidence
  from Training Data
Vibhuti Kumari Narayana Murthy Kavi
Current Machine Translation systems achieve very good results on a growing
variety of language pairs and data sets. However, it is now well known that
they produce fluent translation outputs that often can contain important
meaning errors. Quality Estimation task deals with the estimation of quality of
translations produced by a Machine Translation system without depending on
Reference Translations. A number of approaches have been suggested over the
years. In this paper we show that the parallel corpus used as training data for
training the MT system holds direct clues for estimating the quality of
translations produced by the MT system. Our experiments show that this simple
and direct method holds promise for quality estimation of translations produced
by any purely data driven machine translation system.