Summary of A Federated Approach to Few-shot Hate Speech Detection For Marginalized Communities, by Haotian Ye et al.
A Federated Approach to Few-Shot Hate Speech Detection for Marginalized Communities
by Haotian Ye, Axel Wisiorek, Antonis Maronikolakis, Özge Alaçam, Hinrich Schütze
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the understudied issue of hate speech online affecting marginalized communities in developing societies with increasing internet penetration. The authors propose a privacy-preserving tool to filter offensive content in native languages, releasing REACT, a collection of high-quality, culture-specific hate speech detection datasets in eight low-resource languages. They also introduce a federated learning (FL) approach for few-shot hate speech detection, utilizing local training on users’ devices while ensuring data privacy and efficiency. The results show the effectiveness of FL across target groups, with personalized client models demonstrating unclear benefits for few-shot learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps protect people from online hate speech in languages they understand best. It creates a special tool to detect and block mean content in native languages. This is important because many people in developing countries are using the internet more often. The tool uses something called federated learning, which means that users’ devices work together without sharing personal information. The results show that this approach works well for different groups of people. |
Keywords
» Artificial intelligence » Federated learning » Few shot
