qhjqhj00 multilingual-toxicity-eval
A New Generation of Perspective API: Efficient Multilingual Character-level Transformers — Alyssa Lees et al. (arXiv:2202.11176, 2022)
What this evaluates
Evaluates multilingual toxicity detection capabilities of text classification models across multiple languages, focusing on production readiness, adversarial robustness, and handling of code-switching and obfuscation.
Datasets
- Production-Multilingual — total 39300000; splits: train (38000000), test (1300000)
- Jigsaw Multilingual Toxic Comments Challenge — total ?; splits: train (-1), test (-1)
Metrics
AUC-ROC(primary) — range: [0, 1]- Area under the Receiver Operating Characteristic curve, measuring the trade-off between true positive rate and false positive rate across all classification thresholds.
Input / output format
Input: Raw text comments in multiple languages (e.g., English, Spanish, French, German, etc.), potentially containing code-switching, emojis, or obfuscated text.
Output: Binary toxicity label or probability score indicating whether the comment is toxic.
Scoring recipe
def compute_auc(y_true, y_pred):
from sklearn.metrics import roc_auc_score
return roc_auc_score(y_true, y_pred)
Common pitfalls
- Training data is heavily skewed towards English, which may bias models toward English performance.
- Kaggle competition winners used ensembling and pseudo-labeling, making direct comparison with single production-ready models unfair.
- Evaluation focuses only on a subset of languages where the production API lacked quality models, not a fully balanced multilingual set.
Evidence (verbatim from paper)
Table 1: Experimental results on the Production-Multilingual dataset. We report AUC-ROC scores. Table 2 reports results on the JMTCC dataset. Our results show that our best UTC† achieves 0.9367 AUC-ROC, outperforming all considered single model baselines, especially a strong state-of-the-art mT5 baseline.
Citation
@misc{lees2022perspective,
title={A New Generation of Perspective API: Efficient Multilingual Character-level Transformers},
author={Alyssa Lees et al.},
year={2022},
note={arXiv:2202.11176}
}
- arXiv: 2202.11176