| Challenge: | a new computational approach to exaggeration detection is needed for non-literal phenomena . a corpus of overstatements (or hyperboles) is used to detect exaggrements . |
| Approach: | They propose a computational approach to detect exaggerated sentences using crowdsourcing data . they build a corpus containing overstatements and then evaluate models trained on HYPO . |
| Outcome: | The proposed approach can detect exaggerated sentences using a crowdsourced dataset. |
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| Challenge: | Recent studies on metaphor and metonymy have focused on hyperbole, but it is a relatively understudied phenomenon in the figurative language processing community. |
| Approach: | They propose to use hyperbole detection to determine whether a sentence is hyperbolic . they also perform statistical and manual analyses of the corpus and address the automatic hyperbola detection task. |
| Outcome: | The proposed dataset consists of 709 hyperbolic sentences with a non-hyperbolic version created by paraphrasing its hyperbolical counterpart. |
HypoGen: Hyperbole Generation with Commonsense and Counterfactual Knowledge (2021.findings-emnlp)
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| Challenge: | despite its abundance, the computational explorations of hyperboles remain under-explored. |
| Approach: | They propose a sentence-level hyperbole generation method that leverages commonsense and counterfactual inference to generate hyperbolic candidates based on the results. |
| Outcome: | The proposed method generates hyperboles with high success rate, intensity, funniness, and creativity. |
Probing for Hyperbole in Pre-Trained Language Models (2023.acl-srw)
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| Challenge: | Hyperbole is a common figure of speech that involves the use of exaggerated language for emphasis or effect. |
| Approach: | They conduct edge and minimal description length probing experiments on three pre-trained language models to explore the extent to which hyperbolic information is encoded . they also annotate 63 hyperbole sentences from the HYPO dataset according to an operational taxonomy to conduct an error analysis to explore encoding of different hyperboli categories. |
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MOVER: Mask, Over-generate and Rank for Hyperbole Generation (2022.naacl-main)
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| Challenge: | despite being a common figure of speech, hyperbole is under-researched in Figurative Language Processing . we use an unsupervised method to generate hyperbolic paraphrases from literal sentences . |
| Approach: | They propose an unsupervised method for hyperbole generation that does not require parallel literal-hyperbole pairs. |
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Image Matters: A New Dataset and Empirical Study for Multimodal Hyperbole Detection (2024.lrec-main)
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| Challenge: | linguistic detection of hyperbole is an important part of understanding human expression . studies on hyperbolic expressions focus on text modality, but social media can be used to detect it . |
| Approach: | They propose to use a multimodal detection dataset to study hyperbole detection . they treat text and image as two modalities and evaluate pre-trained encoders . |
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Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges (2025.acl-long)
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Bolei Ma, Yuting Li, Wei Zhou, Ziwei Gong, Yang Janet Liu, Katja Jasinskaja, Annemarie Friedrich, Julia Hirschberg, Frauke Kreuter, Barbara Plank
| Challenge: | linguistics studies how context influences meaning of language and how people use it to convey implied meanings, emotions, and intentions. |
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Do Neural Language Models Overcome Reporting Bias? (2020.coling-main)
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| Challenge: | Recent studies show that pre-trained language models can overcome reporting bias by estimating the plausibility of rare but unspoken facts. |
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Morphological Inflection: A Reality Check (2023.acl-long)
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| Challenge: | Morphological inflection is a popular task in sub-word NLP with practical and cognitive applications. |
| Approach: | They propose new methods to analyze data sets and evaluate their generalization abilities to better reflect likely use-cases. |
| Outcome: | The proposed methods improve generalizability and reliability of results and improve generalization abilities. |
It’s Morphin’ Time! Combating Linguistic Discrimination with Inflectional Perturbations (2020.acl-main)
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| Challenge: | Existing work on societal bias in NLP focuses on race and gender . linguistic background is a unique attribute that has been largely ignored in the field . |
| Approach: | They examine linguistic background to craft plausible adversarial examples that expose biases in popular NLP models. |
| Outcome: | The proposed model improves robustness without sacrificing performance on clean data. |
Incremental Natural Language Processing: Challenges, Strategies, and Evaluation (C18-1)
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| Challenge: | In this survey, I consolidate and categorize the approaches, identifying similarities and differences in computation and data, and show trade-offs that have to be considered. |
| Approach: | They consolidate and categorize approaches to incremental processing and show trade-offs that have to be considered. |
| Outcome: | The proposed approaches show that they have similarities and differences in computation and data and that they are not trivial. |