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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 arewritten by the same author. We consider the authorship verification problem forboth small and large scale datasets. The underlying small-scale problem has twomain challenges: First, the authors of the documents are unknown to us becauseno previous writing samples are available. Second, the two documents are short(a few hundred to a few thousand words) and may differ considerably in thegenre and/or topic. To solve it we propose transformation encoder to transformone document of the pair into the other. This document transformation generatesa loss which is used as a recognizable feature to verify if the authors of thepair are identical. For the large scale problem where various authors areengaged and more examples are available with larger length, a parallelrecurrent neural network is proposed. It compares the language models of thetwo documents. We evaluate our methods on various types of datasets includingAuthorship Identification datasets of PAN competition, Amazon reviews, andmachine learning articles. Experiments show that both methods achieve stableand competitive performance compared to the baselines.

Experiments with Neural Networks for Small and Large Scale Authorship Verification