Summary of Chinese Offensive Language Detection:current Status and Future Directions, by Yunze Xiao et al.
Chinese Offensive Language Detection:Current Status and Future Directions
by Yunze Xiao, Houda Bouamor, Wajdi Zaghouani
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper addresses the challenge of detecting offensive language, such as hate speech or cyberbullying, on social media platforms in real-time. Despite significant efforts, offensive content persists due to the complexity of languages like Chinese. The study provides an overview of current approaches and benchmarks for detecting offensive language in Chinese, highlighting specific models and tools that can address the unique challenges of this complex language. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps detect hate speech or cyberbullying on social media by developing automatic systems. It focuses on Chinese because it’s a tricky language to process. The goal is to find ways to make these detection systems better for Chinese, considering its cultural and linguistic complexities. |
