Rethinking the Authorship Verification Experimental Setups
Florin Brad, Andrei Manolache, Elena Burceanu, Antonio Bărbălau, Radu Tudor Ionescu, Marius Popescu · 2022
One of the main drivers of the recent advances in authorship verification is the PAN large-scale authorship dataset.Despite generating significant progress in the field, inconsistent performance differences between the closed and open test sets have been reported.To this end, we improve the experimental setup by proposing five new public splits over the PAN dataset, specifically designed to isolate and identify biases related to the text topic and to the author's writing style.We evaluate several BERT-like baselines on these splits, showing that such models are competitive with authorship verification state-of-the-art methods.Furthermore, using explainable AI, we find that these baselines are biased towards named entities.We show that models trained without the named entities obtain better results and generalize better when tested on DarkReddit, our new dataset for authorship verification.Test split O2D2 * O2D2 BERT Naive † Comp.† Closed 93.5 96.4 95.6 75.6 72.2 Clopen 94.0 96.0 97.4 74.1 71.1 Open UA 92.6 92.6 90.2 78.6 68.5 Open UF 91.4 95.1 91.6 79.9 79.0 Open All 80.6 67.5 88.7 75.6 76.9 PAN Closed 93.3 93.5 -74.7 74.2 PAN Open 93.3 94.4 -75.3 74.