  In many cases of machine learning, research suggests that the development of
training data might have a higher relevance than the choice and modelling of
classifiers themselves. Thus, data augmentation methods have been developed to
improve classifiers by artificially created training data. In NLP, there is the
challenge of establishing universal rules for text transformations which
provide new linguistic patterns. In this paper, we present and evaluate a text
generation method suitable to increase the performance of classifiers for long
and short texts. We achieved promising improvements when evaluating short as
well as long text tasks with the enhancement by our text generation method. In
a simulated low data regime additive accuracy gains of up to 15.53% are
achieved. As the current track of these constructed regimes is not universally
applicable, we also show major improvements in several real world low data
tasks (up to +4.84 F1 score). Since we are evaluating the method from many
perspectives, we also observe situations where the method might not be
suitable. We discuss implications and patterns for the successful application
of our approach on different types of datasets.
