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BERT and RoBERTa for Sarcasm Detection: Optimizing Performance through Advanced Fine-tuning
- 1 Jacobs School of Engineering, University of California, San Diego, CA, USA
* Author to whom correspondence should be addressed.
Abstract
This paper presents an advanced approach to sarcasm detection in online discourse using state-of-the-art language models. The study systematically evolves Bidirectional Encoder Representations from Transformers (BERT) and Robustly Optimized BERT Pretraining Approach (RoBERTa) architectures from baseline to optimized versions, demonstrating significant improvements in sarcasm detection accuracy. Utilizing a balanced subset of 30,000 samples from a Reddit sarcasm dataset, the research implements gradual unfreezing, adaptive learning rates, and sophisticated regularization techniques. The final RoBERTa model achieves 76.80% accuracy, outperforming BERT and showing balanced precision and recall across sarcastic and non-sarcastic classes. The comparative analysis reveals interesting learning dynamics between BERT and RoBERTa, with RoBERTa demonstrating superior performance in later training stages. The study highlights the importance of architectural innovations and advanced training strategies in capturing the nuanced linguistic cues of sarcasm. While computational constraints limited the dataset size, the research provides valuable insights into model behavior and sets a foundation for future work. The paper concludes by discussing potential avenues for advancement, including scaling to larger datasets, exploring multi-modal approaches, and developing more interpretable models, ultimately contributing to the broader field of natural language understanding and affective computing.
Keywords
Sarcasm Detection, Bidirectional Encoder Representations from Transformers, Robustly Optimized BERT Pretraining Approach, Text Classification, Sentiment Analysis.
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Cite this article
Shu,X. (2024). BERT and RoBERTa for Sarcasm Detection: Optimizing Performance through Advanced Fine-tuning. Applied and Computational Engineering,97,1-11.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation
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