@inproceedings{rivera-soto-etal-2021-learning,
title = "Learning Universal Authorship Representations",
author = "Rivera-Soto, Rafael A. and
Miano, Olivia Elizabeth and
Ordonez, Juanita and
Chen, Barry Y. and
Khan, Aleem and
Bishop, Marcus and
Andrews, Nicholas",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.70",
doi = "10.18653/v1/2021.emnlp-main.70",
pages = "913--919",
abstract = "Determining whether two documents were composed by the same author, also known as authorship verification, has traditionally been tackled using statistical methods. Recently, authorship representations learned using neural networks have been found to outperform alternatives, particularly in large-scale settings involving hundreds of thousands of authors. But do such representations learned in a particular domain transfer to other domains? Or are these representations inherently entangled with domain-specific features? To study these questions, we conduct the first large-scale study of cross-domain transfer for authorship verification considering zero-shot transfers involving three disparate domains: Amazon reviews, fanfiction short stories, and Reddit comments. We find that although a surprising degree of transfer is possible between certain domains, it is not so successful between others. We examine properties of these domains that influence generalization and propose simple but effective methods to improve transfer.",
}
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<abstract>Determining whether two documents were composed by the same author, also known as authorship verification, has traditionally been tackled using statistical methods. Recently, authorship representations learned using neural networks have been found to outperform alternatives, particularly in large-scale settings involving hundreds of thousands of authors. But do such representations learned in a particular domain transfer to other domains? Or are these representations inherently entangled with domain-specific features? To study these questions, we conduct the first large-scale study of cross-domain transfer for authorship verification considering zero-shot transfers involving three disparate domains: Amazon reviews, fanfiction short stories, and Reddit comments. We find that although a surprising degree of transfer is possible between certain domains, it is not so successful between others. We examine properties of these domains that influence generalization and propose simple but effective methods to improve transfer.</abstract>
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%0 Conference Proceedings
%T Learning Universal Authorship Representations
%A Rivera-Soto, Rafael A.
%A Miano, Olivia Elizabeth
%A Ordonez, Juanita
%A Chen, Barry Y.
%A Khan, Aleem
%A Bishop, Marcus
%A Andrews, Nicholas
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F rivera-soto-etal-2021-learning
%X Determining whether two documents were composed by the same author, also known as authorship verification, has traditionally been tackled using statistical methods. Recently, authorship representations learned using neural networks have been found to outperform alternatives, particularly in large-scale settings involving hundreds of thousands of authors. But do such representations learned in a particular domain transfer to other domains? Or are these representations inherently entangled with domain-specific features? To study these questions, we conduct the first large-scale study of cross-domain transfer for authorship verification considering zero-shot transfers involving three disparate domains: Amazon reviews, fanfiction short stories, and Reddit comments. We find that although a surprising degree of transfer is possible between certain domains, it is not so successful between others. We examine properties of these domains that influence generalization and propose simple but effective methods to improve transfer.
%R 10.18653/v1/2021.emnlp-main.70
%U https://aclanthology.org/2021.emnlp-main.70
%U https://doi.org/10.18653/v1/2021.emnlp-main.70
%P 913-919
Markdown (Informal)
[Learning Universal Authorship Representations](https://aclanthology.org/2021.emnlp-main.70) (Rivera-Soto et al., EMNLP 2021)
ACL
- Rafael A. Rivera-Soto, Olivia Elizabeth Miano, Juanita Ordonez, Barry Y. Chen, Aleem Khan, Marcus Bishop, and Nicholas Andrews. 2021. Learning Universal Authorship Representations. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 913–919, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.