@inproceedings{johansson-2024-happens-dataset,
title = "What Happens to a Dataset Transformed by a Projection-based Concept Removal Method?",
author = "Johansson, Richard",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1520",
pages = "17486--17492",
abstract = "We investigate the behavior of methods using linear projections to remove information about a concept from a language representation, and we consider the question of what happens to a dataset transformed by such a method. A theoretical analysis and experiments on real-world and synthetic data show that these methods inject strong statistical dependencies into the transformed datasets. After applying such a method, the representation space is highly structured: in the transformed space, an instance tends to be located near instances of the opposite label. As a consequence, the original labeling can in some cases be reconstructed by applying an anti-clustering method.",
}
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%0 Conference Proceedings
%T What Happens to a Dataset Transformed by a Projection-based Concept Removal Method?
%A Johansson, Richard
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F johansson-2024-happens-dataset
%X We investigate the behavior of methods using linear projections to remove information about a concept from a language representation, and we consider the question of what happens to a dataset transformed by such a method. A theoretical analysis and experiments on real-world and synthetic data show that these methods inject strong statistical dependencies into the transformed datasets. After applying such a method, the representation space is highly structured: in the transformed space, an instance tends to be located near instances of the opposite label. As a consequence, the original labeling can in some cases be reconstructed by applying an anti-clustering method.
%U https://aclanthology.org/2024.lrec-main.1520
%P 17486-17492
Markdown (Informal)
[What Happens to a Dataset Transformed by a Projection-based Concept Removal Method?](https://aclanthology.org/2024.lrec-main.1520) (Johansson, LREC-COLING 2024)
ACL