@inproceedings{brad-etal-2022-rethinking,
title = "Rethinking the Authorship Verification Experimental Setups",
author = "Brad, Florin and
Manolache, Andrei and
Burceanu, Elena and
Barbalau, Antonio and
Ionescu, Radu Tudor and
Popescu, Marius",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.380",
doi = "10.18653/v1/2022.emnlp-main.380",
pages = "5634--5643",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Rethinking the Authorship Verification Experimental Setups
%A Brad, Florin
%A Manolache, Andrei
%A Burceanu, Elena
%A Barbalau, Antonio
%A Ionescu, Radu Tudor
%A Popescu, Marius
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F brad-etal-2022-rethinking
%X 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.
%R 10.18653/v1/2022.emnlp-main.380
%U https://aclanthology.org/2022.emnlp-main.380
%U https://doi.org/10.18653/v1/2022.emnlp-main.380
%P 5634-5643
Markdown (Informal)
[Rethinking the Authorship Verification Experimental Setups](https://aclanthology.org/2022.emnlp-main.380) (Brad et al., EMNLP 2022)
ACL
- Florin Brad, Andrei Manolache, Elena Burceanu, Antonio Barbalau, Radu Tudor Ionescu, and Marius Popescu. 2022. Rethinking the Authorship Verification Experimental Setups. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 5634–5643, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.