mlsanityData to 14 Oct 2026

Metaphor Detection with Effective Context Denoising

arXiv 2302.05611 ↗First posted 11 Feb 2023cs.CLcited by 13

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Abstract (arXiv)

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

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 8 of its 28 references that are in the map. First posted on arXiv 11 Feb 2023.

Builds on

8 of 28 references.

References · 8

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  2. 2016Deep Biaffine Attention for Neural Dependency Parsingcited by 334
  3. 2020Relational Graph Attention Network for Aspect-based Sentiment Analysiscited by 104
  4. 2021MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theoriescited by 46
  5. 2018Neural Metaphor Detection in Contextcited by 34
  6. 2021Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewritingcited by 30
  7. 2023The Secret of Metaphor on Expressing Stronger Emotioncited by 9
  8. 2022Nominal Metaphor Generation with Multitask Learningcited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 5; 2025: 2; 2026: 3.

13 papers.

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