Papers › Metaphor Detection with Effective Context Denoising
Metaphor Detection with Effective Context Denoising
Shun Wang, Yucheng Li, Chenghua Lin, Loïc Barrault, Frank Guerin
We propose a novel RoBERTa-based model, RoPPT, which introduces a target-oriented parse tree structure in metaphor detection. Compared to existing models, RoPPT focuses on semantically relevant information and achieves the state-of-the-art on several main metaphor datasets. We also compare our approach against several popular denoising and pruning methods, demonstrating the effectiveness of our approach in context denoising. Our code and dataset can be found at https://github.com/MajiBear000/RoPPT
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