Work

FL-MSCL: A Unified Figurative Language Detection Model Driven by Multi-Type Signals and Contrastive Learning

Year: 2026

Type: conference-paper

Abstract: 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 Stateof-the-Art (SOTA) methods, indicating consistent advantages in cross-category generalization and category-specific detection.

Source: Proceedings of the conference - Association for Computational Linguistics. Meeting

DOI: 10.18653/v1/2026.acl-short.57

Authors: Lu Shijia, Fumiyo Fukumoto, Huang Xiaoxi, Yoshimi Suzuki

Institutions: Meiji University, University of Yamanashi

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: Ministry of Education, India, Japan Society for the Promotion of Science

Awards: Implicit Emotion Model with Domain Specific Senses and Text Coherence, Multi-label Short Text Classification via Text and Domain Label Augmentation Aligned with Semantic Granularity