Multilingual Multi-Figurative Language Detection (2023)

Abstract: 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.

## introduction figurative language is ubiquitous in human language, allows us to convey abstract concepts and emotions, and has been embedded intimately in our cultures and behaviors @xcite . in the hyperbolic sentence "my heart failed a few times while waiting for the result.", the expression "my heart failed a few times" is not a literal heart-stop, it exaggerates the mood of when waiting for a possibly important result, thereby vividly showing anxiety. recent years have seen a lot of interest in figurative language processing in the nlp community, including the successful organization of dedicated workshops @xcite @xcite . there are many works focusing on figurative language detection, mostly in english, including hyperbole @xcite , metonymy @xcite , metaphor @xcite , idiom @xcite and sarcasm @xcite . in addition, researchers have started start to pay attention to figurative language detection in a multilingual scenario @xcite @xcite , where models can exploit cross-lingual knowledge transfer @xcite . nonetheless, detection tasks for different figures of speech are usually studied independently of each other, which leads to having to train separate models for each figure of speech. however, different figures of speech are often related to each other, and therefore models can thus potentially benefit from cross-figurative knowledge transfer, as empirically shown by @xcite in a monolingual setting for english. in this paper we investigate how these related detection tasks can be connected and modelled jointly in a multilingual way (see reference description m-lang m-fig m-task level troiano et al. (2018) hyperbole detection in english ✗ ✗ ✗ sentence tedeschi et al. (2022) multilingual idiom detection ✓ ✗ ✗ word tayyar madabushi et al. (2022) multilingual idiom detection ✓ ✗ ✗ sentence aghazadeh et al. (2022) multilingual metaphor detection ✓ ✗ ✗ sentence lai and nissim (2022) multi-figurative language generation in english ✗ ✓ ✓ sentence our work multilingual multi-figurative language detection ✓ ✓ ✓ sentence generally, a multi-task framework consists of shared modules and task-specific modules. with the development of pre-trained language models (plms), prompt learning offers the opportunity to model multiple tasks in a framework that does not require task-dependent parameters @xcite @xcite . with this method, task-specific language instructions are predefined and used to guide the model to handle different tasks. in practice, we first formalize the figurative language detection task as a text-to-text generation problem, where the input is the source sentence while the target is a textual label (e.g. "literal" or "idiomatic"). this method thus enables us to train our models in a sequence-to-sequence (seq2seq) fashion. we then prepend the prompt template to source sentences from various tasks when feeding them into the model. this connects multiple figures of speech and languages in a unified framework, also leading to a better understanding of how to jointly model tasks related to each other. we perform extensive experiments on three figures of speech: hyperbole, idiom, and metaphor, involving seven languages (english:en, chinese:zh, german:de, spanish:es, italian:it, farsi:fa, and russian:ru). our main contributions are as follows: (i) we introduce the novel task of multilingual multifigurative language detection, wherein we explore the potential of joint modelling. (ii) we introduce a multitask and multilingual framework based on prompt learning, which unifies interrelated detection tasks without task-nor language-specific modules. (iii) we evaluate the model's generalization capabilities across a range of figures of speech and languages: extensive experiments are run for different settings, including in-language, zero-shot, cross-lingual, and cross-figurative to show how the unified framework performs. (iv) our framework may serve as a blueprint for joint modelling of other interrelated tasks, such as the detection of hate speech @xcite , offensive and abusive language @xcite , toxicity @xcite , as well as fake news and ai-generated content @xcite . we have released our code and all preprocessed dataset. foot_0 ## related work we briefly introduce the background of figurative language detection, from feature engineering to neural-based approaches, as well as prompt-based learning with plms. ## multilingual multi-figurative model we propose a multitask and multilingual framework based on template-based prompt learning for figurative language detection. ## conclusions we introduced a multilingual multi-figurative language understanding benchmark that focuses on sentence-level figurative language detection, involving three common figures of speech and seven languages. based on prompt learning, we proposed a framework to unify the interrelated detection tasks across multiple figures of speech and languages using a plm, while having no task-or language-specific modules. we further analyzed the generalization of the model across different figures of speech and languages. our unified model benefits from cross-lingual and cross-figurative knowledge transfer in sentence-level detection. it is natural to explore fine-grained detection at the word-level in future work, as well as language generation in multilingual and multifigurative scenarios. this approach can also serve as a blueprint for the joint modelling of other interrelated tasks.