Multilingual Multi-Figurative Language Detection
This addresses the need for multilingual and multi-figurative language detection in NLP, which is incremental as it extends existing work to a broader setting.
The paper tackles the problem of detecting multiple figures of speech in multilingual texts, which is understudied, by introducing a framework based on template-based prompt learning that unifies detection tasks across three figures of speech and seven languages. Experimental results show it outperforms strong baselines.
Figures of speech help people express abstract concepts and evoke stronger emotions than literal expressions, thereby making texts more creative and engaging. Due to its pervasive and fundamental character, figurative language understanding has been addressed in Natural Language Processing, but it's highly understudied in a multilingual setting and when considering more than one figure of speech at the same time. To bridge this gap, we introduce multilingual multi-figurative language modelling, and provide a benchmark for sentence-level figurative language detection, covering three common figures of speech and seven languages. Specifically, we develop a framework for figurative language detection based on template-based prompt learning. In so doing, we unify multiple detection tasks that are interrelated across multiple figures of speech and languages, without requiring task- or language-specific modules. Experimental results show that our framework outperforms several strong baselines and may serve as a blueprint for the joint modelling of other interrelated tasks.