mlsanityData to 7 Oct 2026

Figurative Language in Recognizing Textual Entailment

arXiv 2106.01195 ↗First posted 2 Jun 2021cs.CL cs.AIcited by 26

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Abstract (arXiv)

We introduce a collection of recognizing textual entailment (RTE) datasets focused on figurative language. We leverage five existing datasets annotated for a variety of figurative language -- simile, metaphor, and irony -- and frame them into over 12,500 RTE examples.We evaluate how well state-of-the-art models trained on popular RTE datasets capture different aspects of figurative language. Our results and analyses indicate that these models might not sufficiently capture figurative language, struggling to perform pragmatic inference and reasoning about world knowledge. Ultimately, our datasets provide a challenging testbed for evaluating RTE models.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 15 of its 50 references that are in the map. First posted on arXiv 2 Jun 2021.

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15 of 50 references.

References · 15

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  7. 2020Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESuppositioncited by 76
  8. 2019Probing Natural Language Inference Models through Semantic Fragmentscited by 64
  9. 2021MERMAID: Metaphor Generation with Symbolism and Discriminative Decodingcited by 49
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3 more
  1. 2017Sarcasm SIGN: Interpreting Sarcasm with Sentiment Based Monolingual Machine Translationcited by 18
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  3. 2018"With 1 follower I must be AWESOME :P". Exploring the role of irony markers in irony recognitioncited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 7; 2023: 6; 2024: 4; 2025: 4; 2026: 4.

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  1. 2023Can Large Language Models Transform Computational Social Science?cited by 245
  2. 2022FLUTE: Figurative Language Understanding through Textual Explanationscited by 78
  3. 2022Testing the Ability of Language Models to Interpret Figurative Languagecited by 67
  4. 2023Natural Language Reasoning, A Surveycited by 61
  5. 2023MemeCap: A Dataset for Captioning and Interpreting Memescited by 46
  6. 2021It's not Rocket Science : Interpreting Figurative Language in Narrativescited by 39
  7. 2024STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMscited by 31
  8. 2022Can Pre-trained Language Models Interpret Similes as Smart as Human?cited by 13
  9. 2022Improving Large-scale Paraphrase Acquisition and Generationcited by 8
  10. 2022Just-DREAM-about-it: Figurative Language Understanding with DREAM-FLUTEcited by 8
  11. 2024A Novel Cartography-Based Curriculum Learning Method Applied on RoNLI: The First Romanian Natural Language Inference Corpuscited by 8
  12. 2025Metaphor and Large Language Models: When Surface Features Matter More than Deep Understandingcited by 8
14 more
  1. 2023Evaluating Paraphrastic Robustness in Textual Entailment Modelscited by 7
  2. 2024Meta4XNLI: A Crosslingual Parallel Corpus for Metaphor Detection and Interpretationcited by 5
  3. 2025Progressive Binarization with Semi-Structured Pruning for LLMscited by 5
  4. 2022Getting BART to Ride the Idiomatic Train: Learning to Represent Idiomatic Expressionscited by 4
  5. 2025BTC-LLM: Efficient Sub-1-Bit LLM Quantization via Learnable Transformation and Binary Codebookcited by 4
  6. 2022Detecting Euphemisms with Literal Descriptions and Visual Imagerycited by 3
  7. 2026Preferences for Idiomatic Language are Acquired Slowly -- and Forgotten Quickly: A Case Study on Swedishcited by 3
  8. 2023Does the "most sinfully decadent cake ever" taste good? Answering Yes/No Questions from Figurative Contextscited by 2
  9. 2025Large Vision-Language Models for Knowledge-Grounded Data Annotation of Memescited by 2
  10. 2023Generating Continuations in Multilingual Idiomatic Contextscited by 1
  11. 2024MetaphorShare: A Dynamic Collaborative Repository of Open Metaphor Datasetscited by 1
  12. 2026D2Quant: Accurate Low-bit Post-Training Weight Quantization for LLMscited by 1
  13. 2026SinFoS: A Parallel Dataset for Translating Sinhala Figures of Speechcited by 1
  14. 2026As Easy as Rocket Science: Assessing the Ability of Large Language Models to Interpret Negation in Figurative Languagecited by 0