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.

本研究提出多语言多种修辞语言建模的方法，并在三种常见的修辞语言和七种语言中提供句子级修辞语言检测的基准，实验结果表明该框架优于几种强基线，并可作为其他相关任务的联合建模的蓝图。

多语言多比喻语言检测

Multilingual Multi-Figurative Language Detection

Figurative language generation is the task of reformulating a given text in
the desired figure of speech while still being faithful to the original
context. We take the first step towards multi-figurative language modelling by
providing a benchmark for the automatic generation of five common figurative
forms in English. We train mFLAG employing a scheme for multi-figurative
language pre-training on top of BART, and a mechanism for injecting the target
figurative information into the encoder; this enables the generation of text
with the target figurative form from another figurative form without parallel
figurative-figurative sentence pairs. Our approach outperforms all strong
baselines. We also offer some qualitative analysis and reflections on the
relationship between the different figures of speech.

提出了一种基于 BART 的 multi-figurative 语言预训练框架 mFLAG，同时提供了五种常见英文比喻形式的自动生成基准，实验结果表明该方法优于强基线，并对不同语言形式之间的关系进行了一些定性分析和思考。