  We propose two models for a special case of authorship verification problem.
The task is to investigate whether the two documents of a given pair are
written by the same author. We consider the authorship verification problem for
both small and large scale datasets. The underlying small-scale problem has two
main challenges: First, the authors of the documents are unknown to us because
no previous writing samples are available. Second, the two documents are short
(a few hundred to a few thousand words) and may differ considerably in the
genre and/or topic. To solve it we propose transformation encoder to transform
one document of the pair into the other. This document transformation generates
a loss which is used as a recognizable feature to verify if the authors of the
pair are identical. For the large scale problem where various authors are
engaged and more examples are available with larger length, a parallel
recurrent neural network is proposed. It compares the language models of the
two documents. We evaluate our methods on various types of datasets including
Authorship Identification datasets of PAN competition, Amazon reviews, and
machine learning articles. Experiments show that both methods achieve stable
and competitive performance compared to the baselines.
