Facilitating Fine-grained Detection of Chinese Toxic Language: Hierarchical Taxonomy, Resources, and Benchmark
🎉2024.5 Our proposed dataset, ToxiCN, has been adopted by the international evaluation CLEF 2024: Multilingual Text Detoxification as the sole Chinese data source. Report
Here we list some of our team's work on toxic language detection. Feel free to follow!
- Towards Comprehensive Detection of Chinese Harmful Meme (NeurIPS2024). In this paper, we present the definition of Chinese Harmful Meme Detection to align with the Chinese online environment. and present ToxiCN MM, the first Chinese harmful meme dataset. paper repo
- PclGPT: A Large Language Model for Patronizing and Condescending Language Detection (EMNLP2024 findings). In this paper, we focus on a specific type of implicit toxic bias, patronizing and condescending language (PCL), and leverage LLMs to detect it. paper repo
- Towards Patronizing and Condescending Language in Chinese Videos: A Multimodal Dataset and Detector (ICASSP2025). In this paper, we introduce the PCL MM dataset, the first Chinese multimodal dataset for PCL, and propose the MultiPCL framework for detection. paper repo
The paper has been accepted in ACL 2023 (main conference, long paper). Paper
☠️ Warning: The samples presented by this paper may be considered offensive or vulgar.
The opinions and findings contained in the samples of our presented dataset should not be interpreted as representing the views expressed or implied by the authors. We acknowledge the risk of malicious actors attempting to reverse-engineer comments. We sincerely hope that users will employ the dataset responsibly and appropriately, avoiding misuse or abuse. We believe the benefits of our proposed resources outweigh the associated risks. All resources are intended solely for scientific research and are prohibited from commercial use.
we introduce a hierarchical taxonomy Monitor Toxic Frame. Based on the taxonomy, the posts are progressively divided into diverse granularities as follows: (I) Whether Toxic, (II) Toxic Type (general offensive language or hate speech), (III) Targeted Group, (IV) Expression Category (explicitness, implicitness, or reporting).
We conduct a fine-grained annotation of posts crawled from Zhihu and Tieba, including both direct and indirect toxic samples. And ToxiCN dataset is presented, which has 12k comments containing Sexism, Racism, Regional Bias, Anti-LGBTQ, and Others. The dataset is presented in ToxiCN_1.0.csv. Here we simply describe each fine-grain label.
| Label | Description |
|---|---|
| toxic | Identify if a comment is toxic (1) or non-toxic (0). |
| toxic_type | non-toxic: 0, general offensive language: 1, hate speech: 2 |
| expression | non-hate: 0, explicit hate speech: 1, implicit hate speech: 2, reporting: 3 |
| target (a list) | LGBTQ: Index 0, Region: Index 1, Sexism: Index 2, Racism: Index 3, others: Index 4, non-hate: Index 5 |
See https://github.com/DUT-lujunyu/ToxiCN/tree/main/ToxiCN_ex/ToxiCN/lexicon
We present a migratable benchmark of Toxic Knowledge Enhancement (TKE), enriching the text representation. The code is shown in modeling_bert.py, which is based on transformers 3.1.0.
This work is licensed under a Creative Commons Attribution- NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).
If you want to use the resources, please cite the following paper:
@inproceedings{lu-etal-2023-facilitating,
title = "Facilitating Fine-grained Detection of {C}hinese Toxic Language: Hierarchical Taxonomy, Resources, and Benchmarks",
author = "Lu, Junyu and
Xu, Bo and
Zhang, Xiaokun and
Min, Changrong and
Yang, Liang and
Lin, Hongfei",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.898",
doi = "10.18653/v1/2023.acl-long.898",
pages = "16235--16250",
}