Scaling Up Authorship Attribution

Jacob Striebel, Abishek Edikala, Ethan Irby, Alex Rosenfeld, J. Gage, Daniel Dakota, Sandra Kübler

Abstract
We describe our system for authorship attribution in the IARPA HIATUS program. We describe the model and compute infrastructure developed to satisfy the set of technical constraints imposed by IARPA, including runtime limits as well as other constraints related to the ultimate use case. One use-case constraint concerns the explainability of the features used in the system. For this reason, we integrate features from frame semantic parsing, as they are both interpretable and difficult for adversaries to evade. One trade-off with using such features, however, is that more sophisticated feature representations require more complicated architectures, which limit usefulness in time-sensitive and constrained compute environments. We propose an approach to increase the efficiency of frame semantic parsing through an analysis of parallelization and beam search sizes. Our approach results in a system that is approximately 8.37x faster than the base system with a minimal effect on accuracy.
Anthology ID:
2024.naacl-industry.24
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Yi Yang, Aida Davani, Avi Sil, Anoop Kumar
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
295–302
Language:
URL:
https://aclanthology.org/2024.naacl-industry.24
DOI:
10.18653/v1/2024.naacl-industry.24
Bibkey:
Cite (ACL):
Jacob Striebel, Abishek Edikala, Ethan Irby, Alex Rosenfeld, J. Gage, Daniel Dakota, and Sandra Kübler. 2024. Scaling Up Authorship Attribution. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track), pages 295–302, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Scaling Up Authorship Attribution (Striebel et al., NAACL 2024)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/2024.naacl-industry.24.pdf
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