Papers with Stylistic Analysis

6 papers
Chandler: An Explainable Sarcastic Response Generator (2021.emnlp-demo)

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Challenge: sarcasm generators assume intended meaning is opposite of literal meaning . sarcastically generated responses are more specific and coherent to input .
Approach: They propose a system that generates sarcastic responses to a given utterance . they ground their generation process on a formal theory that unambiguously differentiates .
Outcome: The proposed system generates sarcastic responses to a given utterance.
Dying or Departing? Euphemism Detection for Death Discourse in Historical Texts (2025.coling-main)

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Challenge: euphemisms are a linguistic device used to soften discussions of uncomfortable topics . euphorias are used to refer to death in a less direct manner during a period of secularization .
Approach: They propose to use a corpus of Danish and Norwegian novels to detect death-related euphemisms . they use pre-trained language models to detect euphoric and literal references to death .
Outcome: The proposed method improves on state-of-the-art language models.
Code-Switching Patterns Can Be an Effective Route to Improve Performance of Downstream NLP Applications: A Case Study of Humour, Sarcasm and Hate Speech Detection (2020.acl-main)

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Challenge: In this paper, we demonstrate how code-switching patterns can be utilised to improve various downstream NLP applications.
Approach: They propose to use code-switching patterns to improve various downstream NLP applications.
Outcome: The proposed features can improve humour, sarcasm and hate speech detection tasks.
Construction Artifacts in Metaphor Identification Datasets (2023.emnlp-main)

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Challenge: Existing metaphor identification datasets can be gamed by completely ignoring the potential metaphorical expression or the context in which it occurs.
Approach: They show that existing metaphor identification datasets can be gamed by fully ignoring the potential metaphorical expression or the context in which it occurs.
Outcome: The proposed system can be gamed by fully ignoring the potential metaphorical expression or the context in which it occurs.
MetFuse: Figurative Fusion between Metonymy and Metaphor (2026.acl-long)

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Challenge: Metonymy and metaphor are two fundamental linguistic phenomena in figurative language that involve concept mapping.
Approach: They propose a framework that transforms a literal sentence into three figurative variants . they propose 'metonymic, metaphoric, and hybrid' datasets that can be used to map metonymy and metaphor .
Outcome: The proposed framework transforms a literal sentence into three figurative variants . hybrid examples yield the largest gains on metonymy tasks, the study shows .
Exploring Concreteness Through a Figurative Lens (2026.acl-long)

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Challenge: Static concreteness ratings are widely used in NLP, yet a word’s concreteness can shift with context, especially in figurative language such as metaphor, where common concrete nouns can take abstract interpretations.
Approach: They conduct a layer-wise and geometric analysis of LLM hidden representations across four model families to examine how models distinguish literal vs. figurative usage.
Outcome: The results show that LLMs separate literal and figurative usage in early layers and that mid-to-late layers compress concreteness into a one-dimensional direction consistent across models.

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