Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·Jun 9·5 min read
TeamHerald@CHIPSAL 2026: Hate Speech Detection and Sentiment Analysis of Nepali Memes using Transformer-based Architectures and Ensemble Learning
TL;DR
Hate speech detection in Nepali memes is challenging due to code-mixing and limited resources. This study utilized Transformer-based architectures and ensemble learning techniques, specifically Hard and Soft Voting, to analyze hate speech and sentiment.
✦ Why It Matters
Engineers can leverage ensemble learning techniques to improve hate speech detection in multilingual contexts.
Key Takeaways
- Explore ensemble learning to enhance model performance.
- Utilize OCR for text extraction in visual content.
- Consider code-mixing challenges in language processing.
- Evaluate multiple models for robust analysis.
Full Summary
Hate speech detection in online content, particularly in the Nepali language, faces significant challenges due to frequent code-mixing (the blending of languages) and a scarcity of baseline resources for analysis. This research focused on a text-centric approach, employing Optical Character Recognition (OCR) to extract text from memes and utilizing Transformer-based architectures, which are advanced neural network models known for their effectiveness in natural language processing tasks.
Six distinct models were evaluated, and the study compared the performance of Hard Voting and Soft Voting ensemble strategies for two tasks: binary hate speech classification and sentiment analysis. Results indicated that the ensemble methods significantly enhanced detection accuracy, although specific performance metrics were not disclosed.
These findings suggest that combining multiple models can lead to better outcomes in complex language scenarios. The implications of this research are particularly relevant for engineers and researchers working on language processing and social media analysis.