mteb/multi-hatecheck

Tasks

• text-classification

Citation text from the card

@inproceedings{rottger-etal-2021-hatecheck,
  abstract = {Detecting online hate is a difficult task that even state-of-the-art models struggle with. Typically, hate speech detection models are evaluated by measuring their performance on held-out test data using metrics such as accuracy and F1 score. However, this approach makes it difficult to identify specific model weak points. It also risks overestimating generalisable model performance due to increasingly well-evidenced systematic gaps and biases in hate speech datasets. To enable more targeted diagnostic insights, we introduce HateCheck, a suite of functional tests for hate speech detection models. We specify 29 model functionalities motivated by a review of previous research and a series of interviews with civil society stakeholders. We craft test cases for each functionality and validate their quality through a structured annotation process. To illustrate HateCheck{'}s utility, we test near-state-of-the-art transformer models as well as two popular commercial models, revealing critical model weaknesses.},
  address = {Online},
  author = {R{\"o}ttger, Paul  and
Vidgen, Bertie  and
Nguyen, Dong  and
Waseem, Zeerak  and
Margetts, Helen  and
Pierrehumbert, Janet},
  booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  doi = {10.18653/v1/2021.acl-long.4},
  editor = {Zong, C

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