@inproceedings{nguyen-etal-2023-stacking,
title = "Stacking the Odds: Transformer-Based Ensemble for {AI}-Generated Text Detection",
author = "Nguyen, Duke and
Naing, Khaing Myat Noe and
Joshi, Aditya",
editor = "Muresan, Smaranda and
Chen, Vivian and
Casey, Kennington and
David, Vandyke and
Nina, Dethlefs and
Koji, Inoue and
Erik, Ekstedt and
Stefan, Ultes",
booktitle = "Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association",
month = nov,
year = "2023",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.alta-1.22",
pages = "173--178",
abstract = "This paper reports our submission under the team name {`}SynthDetectives{'} to the ALTA 2023 Shared Task. We use a stacking ensemble of Transformers for the task of AI-generated text detection. Our approach is novel in terms of its choice of models in that we use accessible and lightweight models in the ensemble. We show that ensembling the models results in an improved accuracy in comparison with using them individually. Our approach achieves an accuracy score of 0.9555 on the official test data provided by the shared task organisers.",
}
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%0 Conference Proceedings
%T Stacking the Odds: Transformer-Based Ensemble for AI-Generated Text Detection
%A Nguyen, Duke
%A Naing, Khaing Myat Noe
%A Joshi, Aditya
%Y Muresan, Smaranda
%Y Chen, Vivian
%Y Casey, Kennington
%Y David, Vandyke
%Y Nina, Dethlefs
%Y Koji, Inoue
%Y Erik, Ekstedt
%Y Stefan, Ultes
%S Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association
%D 2023
%8 November
%I Association for Computational Linguistics
%C Melbourne, Australia
%F nguyen-etal-2023-stacking
%X This paper reports our submission under the team name ‘SynthDetectives’ to the ALTA 2023 Shared Task. We use a stacking ensemble of Transformers for the task of AI-generated text detection. Our approach is novel in terms of its choice of models in that we use accessible and lightweight models in the ensemble. We show that ensembling the models results in an improved accuracy in comparison with using them individually. Our approach achieves an accuracy score of 0.9555 on the official test data provided by the shared task organisers.
%U https://aclanthology.org/2023.alta-1.22
%P 173-178
Markdown (Informal)
[Stacking the Odds: Transformer-Based Ensemble for AI-Generated Text Detection](https://aclanthology.org/2023.alta-1.22) (Nguyen et al., ALTA 2023)
ACL