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. |
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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. |
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. |
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IMPLI: Investigating NLI Models’ Performance on Figurative Language (2022.acl-long)
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| Challenge: | Understanding figurative language is a difficult area in NLP but is essential for proper understanding. |
| Approach: | They propose to use a dataset to generate 24k semiautomatic pairs and manually create 1.8k gold pairs to evaluate NLI models. |
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It is not a piece of cake for GPT: Explaining Textual Entailment Recognition in the presence of Figurative Language (2025.coling-main)
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| Challenge: | Figure-based language is used to convey opinions, ideas, or emotions in texts and dialogues. |
| Approach: | They evaluate the capabilities of Large Language Models to address TER and generate textual explanations of TER predictions. |
| Outcome: | The proposed model outperforms the open-source models in Zero- and Few-Shot Learning settings and shows significant performance improvements. |
IRFL: Image Recognition of Figurative Language (2023.findings-emnlp)
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| Challenge: | Figures of speech are ubiquitous in many forms of discourse, allowing people to convey complex, abstract ideas and evoke emotion. |
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Testing the Ability of Language Models to Interpret Figurative Language (2022.naacl-main)
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| Challenge: | Existing work on figurative language has not been done on literal language models. |
| Approach: | They propose a Winograd-style task to evaluate figurative phrases with divergent meanings by interpreting paired figurativ phrases with a human input. |
| Outcome: | The proposed task outperforms state-of-the-art models on a nonliteral language understanding task in zero-shot settings. |
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. |
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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. |
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| Outcome: | The results show that the most common figurative expressions are found in Hindi, Indonesian, Javanese, Kannada, Sundanese, Swahili and Yoruba. |
Figurative Language Processing: A Linguistically Informed Feature Analysis of the Behavior of Language Models and Humans (2023.findings-acl)
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| Challenge: | Recent years have witnessed a growing interest in investigating what Transformer-based language models (TLMs) actually learn from training data. |
| Approach: | They propose to use a black-box TLM and two intrinsically transparent white-box models to investigate the performance of figurative language models on sarcasm, similes, idioms, and metaphors. |
| Outcome: | The proposed models perform better than other models on figurative language classification tasks. |
Learning Trajectories of Figurative Language for Pre-Trained Language Models (2025.findings-emnlp)
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| Challenge: | Figures of speech and figures of language are used in everyday communication . however, this imaginative use of words requires a solid understanding of semantics and real-world knowledge. |
| Approach: | They exploit probing tasks to analyse how NLMs recognise figurative language . they find out which layers have a better comprehension of figurativ language based on pre-training data. |
| Outcome: | The proposed model can recognise hyperboles, metaphors, oxymorons and pleonasms . data show which layers have a better comprehension of figurative language . |