Topic-Regularized Authorship Representation Learning

Jitkapat Sawatphol, Nonthakit Chaiwong, Can Udomcharoenchaikit, Sarana Nutanong


Abstract
Authorship attribution is a task that aims to identify the author of a given piece of writing. We aim to develop a generalized solution that can handle a large number of texts from authors and topics unavailable in training data. Previous studies have proposed strategies to address only either unseen authors or unseen topics. Authorship representation learning has been shown to work in open-set environments with a large number of unseen authors but has not been explicitly designed for cross-topic environments at the same time. To handle a large number of unseen authors and topics, we propose Authorship Representation Regularization (ARR), a distillation framework that creates authorship representation with reduced reliance on topic-specific information. To assess the performance of our framework, we also propose a cross-topic-open-set evaluation method. Our proposed method has improved performances in the cross-topic-open set setup over baselines in 4 out of 6 cases.
Anthology ID:
2022.emnlp-main.70
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1076–1082
Language:
URL:
https://aclanthology.org/2022.emnlp-main.70
DOI:
Bibkey:
Cite (ACL):
Jitkapat Sawatphol, Nonthakit Chaiwong, Can Udomcharoenchaikit, and Sarana Nutanong. 2022. Topic-Regularized Authorship Representation Learning. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 1076–1082, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Topic-Regularized Authorship Representation Learning (Sawatphol et al., EMNLP 2022)
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PDF:
https://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.70.pdf