Summary of Ensemble Of Pre-trained Language Models and Data Augmentation For Hate Speech Detection From Arabic Tweets, by Kheir Eddine Daouadi et al.
Ensemble of pre-trained language models and data augmentation for hate speech detection from Arabic tweets
by Kheir Eddine Daouadi, Yaakoub Boualleg, Kheir Eddine Haouaouchi
First submitted to arxiv on: 2 Jul 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 proposes a novel approach to classify hate speech from Arabic tweets using ensemble learning and semi-supervised learning techniques. The proposed method leverages pre-trained language models and manual labeling, achieving state-of-the-art performance on a benchmark dataset. Specifically, the authors show that their approach outperforms existing works in both accuracy and robustness, particularly when dealing with imbalanced data. By improving hate speech detection from Arabic tweets, this study contributes to the development of more effective solutions for combating online hate speech. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding ways to automatically identify hateful language on social media, especially in Arabic. Right now, many systems are not very good at doing this, and that’s a problem because hate speech can be harmful. To solve this issue, the authors came up with a new method that combines different techniques to analyze tweets. They tested their approach on a big dataset of Arabic tweets and found that it works really well, especially when they used language models trained on lots of data. This is important because it could help us better detect hate speech online. |
Keywords
» Artificial intelligence » Semi supervised
