Congrui Huang
2024
ToxiCraft: A Novel Framework for Synthetic Generation of Harmful Information
Zheng Hui
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Zhaoxiao Guo
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Hang Zhao
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Juanyong Duan
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Congrui Huang
Findings of the Association for Computational Linguistics: EMNLP 2024
In different NLP tasks, detecting harmful content is crucial for online environments, especially with the growing influence of social media. However, previous research has two main issues: 1) a lack of data in low-resource settings, and 2) inconsistent definitions and criteria for judging harmful content, requiring classification models to be robust to spurious features and diverse. We propose Toxicraft, a novel framework for synthesizing datasets of harmful information to address these weaknesses. With only a small amount of seed data, our framework can generate a wide variety of synthetic, yet remarkably realistic, examples of toxic information. Experimentation across various datasets showcases a notable enhancement in detection model robustness and adaptability, surpassing or close to the gold labels.
2011
Timeline Generation through Evolutionary Trans-Temporal Summarization
Rui Yan
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Liang Kong
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Congrui Huang
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Xiaojun Wan
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Xiaoming Li
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Yan Zhang
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing
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Co-authors
- Zheng Hui 1
- Zhaoxiao Guo 1
- Hang Zhao 1
- Juanyong Duan 1
- Rui Yan 1
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