We propose an approach to building models that incorporates the best of both modes: training on native texts to facilitate the possibility of training from large amounts of data without the need for annotation, but using the correction mode with a modest amount of annotated ESL data so that the model can adapt to writers' errors.
at evaluation time, these models can make use of the potentially erroneous information provided by the writer.