| Challenge: | Figures of speech are ubiquitous in many forms of discourse, allowing people to convey complex, abstract ideas and evoke emotion. |
| Approach: | They develop a dataset for multimodal figurative language understanding using human annotation and an automatic pipeline to generate a multimodal dataset. |
| Outcome: | The proposed dataset performs better than human vision and language models compared with a human dataset . |
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Figurative Language in Recognizing Textual Entailment (2021.findings-acl)
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| Challenge: | Existing RTE models struggle to capture figurative language, despite its ubiquity, it remains a bottleneck in automatic text understanding. |
| Approach: | They propose to frame five existing figurative language datasets into over 12,500 RTE examples. |
| Outcome: | The proposed models struggle to perform pragmatic inference and reasoning about world knowledge. |
Semantic Contrastive Adaptation for Multimodal Figurative Language Understanding (2026.acl-srw)
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| Challenge: | Existing models for understanding figurative language in images perform well on literal recognition but fail on multimodal figurativ benchmarks. |
| Approach: | They propose a model that adapts to idiomatic and figurative language using literal alignment bias rather than limited model capacity. |
| Outcome: | The proposed model generalizes across five idiom-rich languages despite being trained on English supervision. |
FLUTE: Figurative Language Understanding through Textual Explanations (2022.emnlp-main)
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| Challenge: | Figurative language understanding is a recognizing textual entailment task, but lacks data for figurative language. |
| Approach: | They propose to use a dataset to analyze figurative NLI instances with explanations to improve models' performance. |
| Outcome: | The proposed dataset can scale up models even for figurative language using human annotations. |
FigMemes: A Dataset for Figurative Language Identification in Politically-Opinionated Memes (2022.emnlp-main)
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| Challenge: | FigMemes is a dataset for figurative language classification in politically-opinionated memes. |
| Approach: | They propose to use figurative language classification to identify politically-opinionated memes by analyzing their datasets and comparing them to other machine learning models. |
| Outcome: | The proposed dataset includes annotations of six commonly used types of figurative language in politically-opinionated memes and a wide range of topics and visual styles. |
Multilingual Multi-Figurative Language Detection (2023.findings-acl)
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| Challenge: | Figures of speech help people express abstract concepts and emotions, but it's understudied in a multilingual setting and when considering more than one figure of speech at the same time. |
| Approach: | They propose a framework for sentence-level figurative language detection based on template-based prompt learning and use it to unify multiple detection tasks that are interrelated across multiple figures of speech and languages. |
| Outcome: | The proposed framework outperforms baselines and may serve as blueprint for the joint modelling of other interrelated tasks. |
Multi-lingual and Multi-cultural Figurative Language Understanding (2023.findings-acl)
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Anubha Kabra, Emmy Liu, Simran Khanuja, Alham Fikri Aji, Genta Winata, Samuel Cahyawijaya, Anuoluwapo Aremu, Perez Ogayo, Graham Neubig
| Challenge: | Figures permeate human communication, but are understudied in NLP. |
| Approach: | They create a figurative language inference dataset for seven languages associated with a variety of cultures, using cultural and regional concepts for figurativ expressions. |
| Outcome: | The results show that the most common figurative expressions are found in Hindi, Indonesian, Javanese, Kannada, Sundanese, Swahili and Yoruba. |
Figuratively Speaking: Authorship Attribution via Multi-Task Figurative Language Modeling (2024.findings-acl)
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| Challenge: | Existing models that detect multiple FL features in text are not effective in authorship attribution tasks. |
| Approach: | They propose a multi-task Figurative Language Model that learns to detect multiple FL features in text at once. |
| Outcome: | The proposed model outperforms specialized binary models in AA tasks or outperformed binary models on three datasets. |
It’s not Rocket Science: Interpreting Figurative Language in Narratives (2022.tacl-1)
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| Challenge: | Existing text representations by design rely on compositionality, while figurative language is often non-compositional. |
| Approach: | They propose to use a pre-trained language model to interpret figurative language types to adopt human strategies for interpreting figurativ language types: inferring meaning from context and relying on constituent words’ literal meanings. |
| Outcome: | The proposed models perform significantly worse than humans on discriminative and generative tasks, bridging the gap from human performance. |
Beyond Understanding: Evaluating the Pragmatic Gap in LLMs’ Cultural Processing of Figurative Language (2026.eacl-long)
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| Challenge: | Using figurative language as a proxy for cultural nuance and local knowledge, large language models struggle with connotative meaning. |
| Approach: | They evaluate large language models' ability to process culturally grounded language . they use figurative language as a proxy for cultural nuance and local knowledge . |
| Outcome: | The proposed models can understand and use figurative expressions that encode local knowledge and social nuance. |
Understanding Figurative Meaning through Explainable Visual Entailment (2025.naacl-long)
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| Challenge: | Existing models for visual entailment and visual question-answering have limited ability to understand figurative meaning in images and captions. |
| Approach: | They propose a task framing the figurative meaning understanding problem as an explainable visual entailment task where the model has to predict whether the image entitles a caption and justify the predicted label with a textual explanation. |
| Outcome: | The proposed dataset contains 6,027 image, caption, label, explanation instances covering five diverse figurative phenomena. |