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Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbations

Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbations

International Conference on Language Resources and Evaluation (LREC), 2022

17 January 2022

Grzegorz Chrupała

Walter Daelemans

ArXiv (abs)PDF HTML Github (1★)

Papers citing "Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbations"

3 / 3 papers shown

Leveraging Diffusion Perturbations for Measuring Fairness in Computer
Vision

Leveraging Diffusion Perturbations for Measuring Fairness in Computer VisionAAAI Conference on Artificial Intelligence (AAAI), 2023

William Berrios

Douwe Kiela

277

3

0

25 Nov 2023

Neural Data-to-Text Generation Based on Small Datasets: Comparing the
Added Value of Two Semi-Supervised Learning Approaches on Top of a Large
Language Model

Neural Data-to-Text Generation Based on Small Datasets: Comparing the Added Value of Two Semi-Supervised Learning Approaches on Top of a Large Language ModelComputational Linguistics (CL), 2022

Chris van der Lee

Thiago Castro Ferreira

Travis J. Wiltshire

240

2

0

14 Jul 2022

Speech Detection Task Against Asian Hate: BERT the Central, While
Data-Centric Studies the Crucial

Speech Detection Task Against Asian Hate: BERT the Central, While Data-Centric Studies the Crucial

220

1

0

05 Jun 2022

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