Parallel data in the domain of interest is the key resource when training a statistical machine translation (SMT) system for a specific purpose. Since ad-hoc manual translation can represent a significant investment in time and money, a prior assesment of the amount of training data required to achieve a satisfactory accuracy level can be very useful. In this work, we show how to predict what the learning curve would look like if we were to manually translate increasing amounts of data.
