  This paper outlines a theoretical framework using which different automatic
metrics can be designed for evaluation of Machine Translation systems. It
introduces the concept of {\em cognitive ease} which depends on {\em adequacy}
and {\em lack of fluency}. Thus, cognitive ease becomes the main parameter to
be measured rather than comprehensibility. The framework allows the components
of cognitive ease to be broken up and computed based on different linguistic
levels etc. Independence of dimensions and linearly combining them provides for
a highly modular approach.
  The paper places the existing automatic methods in an overall framework, to
understand them better and to improve upon them in future. It can also be used
to evaluate the newer types of MT systems, such as speech to speech translation
and discourse translation.
