Facilitating Fine-grained Detection of Chinese Toxic Language: Hierarchical Taxonomy, Resources, and Benchmark

The paper has been accepted in ACL 2023 (main conference, long paper).

☠️ Warning: The samples presented by this paper may be considered offensive or vulgar.

📜 Monitor Toxic Frame

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).

📜 ToxiCN

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 (0) or non-toxic (1).
toxic_type	non-toxic: 0, general offensive language: 1, hate speech: 2
expression	non-hate: 0, explicit hate speech: 1, implicit hate speech: 2
target (a list)	LGBTQ: Index 0, Region: Index 1, Sexism: Index 2, Racism: Index 3, others: Index 4, non-hate: Index 5

📜 Insult Lexicon

The resource is still being collated. We will upload it as soon as possible.

📜 Benchmark

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.

❗️ Ethics Statement

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 hope that the benefits of our proposed resources outweigh their risks. All resources are for scientific research only.

❗️ Licenses

This work is licensed under a Creative Commons Attribution- NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

Poster

Cite

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",
}