FL-MSCL: A Unified Figurative Language Detection Model Driven by Multi-Type Signals and Contrastive Learning
Lu Shijia, Fumiyo Fukumoto Meiji University, Huang Xiaoxi Hangzhou Dianzi University, Yoshimi Suzuki Yamanashi University
原文摘要
Figurative language recognition poses significant challenges in NLP, particularly when distinguishing between fine-grained rhetorical categories such as metaphor, metonymy, and simile. This paper formulates the problem as a four-way sentence-level classification task and proposes FL-MSCL, a unified framework integrating prompt-based knowledge injection with supervised contrastive learning. Experiments across both unified and single-class benchmarks demonstrate that FL-MSCL achieves competitive performance compared to State-of-the-Art (SOTA) methods, indicating consistent advantages in cross-category generalization and category-specific detection.
cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · 2026 · 正式发表