# Figurative Language in Recognizing Textual Entailment

arXiv: https://arxiv.org/abs/2106.01195 · HTML: https://mlsanity.com/paper/2106.01195/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 50 references that are in the map.
- First posted on arXiv 2 Jun 2021.

## Builds on (most cited first, 15 of 15)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [Supervised Learning of Universal Sentence Representations from Natural Language Inference Data](https://mlsanity.com/paper/1705.02364/) (2017)
- [Stress Test Evaluation for Natural Language Inference](https://mlsanity.com/paper/1806.00692/) (2018)
- [Breaking NLI Systems with Sentences that Require Simple Lexical Inferences](https://mlsanity.com/paper/1805.02266/) (2018)
- [Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition](https://mlsanity.com/paper/2004.03066/) (2020)
- [Probing Natural Language Inference Models through Semantic Fragments](https://mlsanity.com/paper/1909.07521/) (2019)
- [MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding](https://mlsanity.com/paper/2103.06779/) (2021)
- [Probing What Different NLP Tasks Teach Machines about Function Word Comprehension](https://mlsanity.com/paper/1904.11544/) (2019)
- [Evaluating Compositionality in Sentence Embeddings](https://mlsanity.com/paper/1802.04302/) (2018)
- [Do Neural Models Learn Systematicity of Monotonicity Inference in Natural Language?](https://mlsanity.com/paper/2004.14839/) (2020)
- [Sarcasm SIGN: Interpreting Sarcasm with Sentiment Based Monolingual Machine Translation](https://mlsanity.com/paper/1704.06836/) (2017)
- [Temporal and Aspectual Entailment](https://mlsanity.com/paper/1904.01297/) (2019)
- ["With 1 follower I must be AWESOME :P". Exploring the role of irony markers in irony recognition](https://mlsanity.com/paper/1804.05253/) (2018)

## Built on by (most cited first, 26 of 26)

- [Can Large Language Models Transform Computational Social Science?](https://mlsanity.com/paper/2305.03514/) (2023)
- [FLUTE: Figurative Language Understanding through Textual Explanations](https://mlsanity.com/paper/2205.12404/) (2022)
- [Testing the Ability of Language Models to Interpret Figurative Language](https://mlsanity.com/paper/2204.12632/) (2022)
- [Natural Language Reasoning, A Survey](https://mlsanity.com/paper/2303.14725/) (2023)
- [MemeCap: A Dataset for Captioning and Interpreting Memes](https://mlsanity.com/paper/2305.13703/) (2023)
- [It's not Rocket Science : Interpreting Figurative Language in Narratives](https://mlsanity.com/paper/2109.00087/) (2021)
- [STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs](https://mlsanity.com/paper/2408.01803/) (2024)
- [Can Pre-trained Language Models Interpret Similes as Smart as Human?](https://mlsanity.com/paper/2203.08452/) (2022)
- [Improving Large-scale Paraphrase Acquisition and Generation](https://mlsanity.com/paper/2210.03235/) (2022)
- [Just-DREAM-about-it: Figurative Language Understanding with DREAM-FLUTE](https://mlsanity.com/paper/2210.16407/) (2022)
- [A Novel Cartography-Based Curriculum Learning Method Applied on RoNLI: The First Romanian Natural Language Inference Corpus](https://mlsanity.com/paper/2405.11877/) (2024)
- [Metaphor and Large Language Models: When Surface Features Matter More than Deep Understanding](https://mlsanity.com/paper/2507.15357/) (2025)
- [Evaluating Paraphrastic Robustness in Textual Entailment Models](https://mlsanity.com/paper/2306.16722/) (2023)
- [Meta4XNLI: A Crosslingual Parallel Corpus for Metaphor Detection and Interpretation](https://mlsanity.com/paper/2404.07053/) (2024)
- [Progressive Binarization with Semi-Structured Pruning for LLMs](https://mlsanity.com/paper/2502.01705/) (2025)
- [Getting BART to Ride the Idiomatic Train: Learning to Represent Idiomatic Expressions](https://mlsanity.com/paper/2207.03679/) (2022)
- [BTC-LLM: Efficient Sub-1-Bit LLM Quantization via Learnable Transformation and Binary Codebook](https://mlsanity.com/paper/2506.12040/) (2025)
- [Detecting Euphemisms with Literal Descriptions and Visual Imagery](https://mlsanity.com/paper/2211.04576/) (2022)
- [Preferences for Idiomatic Language are Acquired Slowly -- and Forgotten Quickly: A Case Study on Swedish](https://mlsanity.com/paper/2602.03484/) (2026)
- [Does the "most sinfully decadent cake ever" taste good? Answering Yes/No Questions from Figurative Contexts](https://mlsanity.com/paper/2309.13748/) (2023)
- [Large Vision-Language Models for Knowledge-Grounded Data Annotation of Memes](https://mlsanity.com/paper/2501.13851/) (2025)
- [D2Quant: Accurate Low-bit Post-Training Weight Quantization for LLMs](https://mlsanity.com/paper/2602.02546/) (2026)
- [Generating Continuations in Multilingual Idiomatic Contexts](https://mlsanity.com/paper/2310.20195/) (2023)
- [MetaphorShare: A Dynamic Collaborative Repository of Open Metaphor Datasets](https://mlsanity.com/paper/2411.18260/) (2024)
- [SinFoS: A Parallel Dataset for Translating Sinhala Figures of Speech](https://mlsanity.com/paper/2602.09866/) (2026)
- [As Easy as Rocket Science: Assessing the Ability of Large Language Models to Interpret Negation in Figurative Language](https://mlsanity.com/paper/2606.18922/) (2026)

## 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.