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FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning

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arxiv 2302.04834 v1 pith:MQIFY7QX submitted 2023-02-09 cs.CL

Yucheng Li , Shun Wang , Chenghua Lin , Frank Guerin , Loïc Barrault This is my paper
classification cs.CL
keywords framebertdetectionframenetmetaphorabilityaccountingachievesattributing
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In this paper, we propose FrameBERT, a RoBERTa-based model that can explicitly learn and incorporate FrameNet Embeddings for concept-level metaphor detection. FrameBERT not only achieves better or comparable performance to the state-of-the-art, but also is more explainable and interpretable compared to existing models, attributing to its ability of accounting for external knowledge of FrameNet.

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Cited by 1 Pith paper

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  1. Exploring Task Performance with Interpretable Models via Sparse Auto-Encoders

    cs.CL 2025-07 conditional novelty 6.0 of 10

    Sparse-autoencoder features from LLMs trigger automatic prompt reformulation, yielding consistent gains on mathematical reasoning and metaphor detection.

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