  Recurrent neural networks (RNNs) are very good at modelling the flow of text,
but typically need to be trained on a far larger corpus than is available for
the PAN 2015 Author Identification task. This paper describes a novel approach
where the output layer of a character-level RNN language model is split into
several independent predictive sub-models, each representing an author, while
the recurrent layer is shared by all. This allows the recurrent layer to model
the language as a whole without over-fitting, while the outputs select aspects
of the underlying model that reflect their author's style. The method proves
competitive, ranking first in two of the four languages.
