| Challenge: | Existing approaches to slang interpretation rely on context but ignore semantic extensions common in slings . a semantically informed slapping framework can be applied to enhancing machine translation of informal language . |
| Approach: | They propose a semantically informed slang interpretation framework that considers contextual and semantic appropriateness of a candidate interpretation for a query s. |
| Outcome: | The proposed framework achieves state-of-the-art accuracy in slang interpretation in English and in other languages. |
Similar Papers
A Computational Framework for Slang Generation (2021.tacl-1)
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| Challenge: | Existing language models trained on large text corpora are biased toward formal language and under-represent slang. |
| Approach: | They propose a framework that models the speaker’s word choice in slang context by relating the conventional and sexist senses of a word while incorporating syntactic and contextual knowledge. |
| Outcome: | The proposed framework outperforms state-of-the-art language models and better predicts the historical emergence of slang word usages from 1960s to 2000s. |
Toward Informal Language Processing: Knowledge of Slang in Large Language Models (2024.naacl-long)
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| Challenge: | Recent advances in large language models (LLMs) have offered a strong potential for natural language systems to process informal language. |
| Approach: | They propose to use movie subtitles to evaluate slang in large language models . they find that smaller LLMs finetuned on the dataset achieve comparable performance . |
| Outcome: | The proposed dataset can be used to evaluate LLMs on slang detection and identification of regional and historical sources for interpretive insights. |
How do Language Models Generate Slang: A Systematic Comparison between Human and Machine-Generated Slang Usages (2025.findings-emnlp)
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| Challenge: | Slang is a commonly used type of informal language that poses a daunting challenge to NLP systems. |
| Approach: | They compare human-attested slang and swiss-generated slurs with machine-generated ones . they find that LLMs have significant knowledge about the creative aspects of sling . |
| Outcome: | The proposed model compares human and machine-generated slang usages to find biases in human perceptions of sling . the results suggest that human-attested slms have significant knowledge about the creative aspects of a language . |
SLANG: New Concept Comprehension of Large Language Models (2024.emnlp-main)
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| Challenge: | Dynamic nature of language limits the adaptability of Large Language Models (LLMs) Traditionally, LLMs are trained on static data, which limits their adaptability . |
| Approach: | They propose a benchmark to integrate novel data and assess LLMs’ ability to comprehend emerging concepts, alongside a causal inference-based approach to enhance LLM comprehension of new phrases and their colloquial context. |
| Outcome: | The proposed model outperforms baseline models in terms of precision and relevance in the comprehension of Internet slang and memes. |
Tracing Semantic Variation in Slang (2022.emnlp-main)
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| Challenge: | Existing approaches to slang semantic variation do not account for the semantic variation of sling among different groups of users. |
| Approach: | They propose to use slang semantic variation models to trace the regional identity of a new emerging sling sense given its historical meanings. |
| Outcome: | The proposed models can predict regional identity of emerging slang word meanings from historical sling dictionary entries. |
Simple Models for Word Formation in Slang (N18-1)
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| Challenge: | slang is a popular vocabulary among young people due to its extragrammatical properties and the rise of social media. |
| Approach: | They propose a data-driven approach coupled with linguistic knowledge to develop generative models for three types of extra-grammatical word formation phenomena abounding in slang: Blends, Clippings, and Reduplicatives. |
| Outcome: | The proposed models show that slang exhibits extragrammatical properties that distinguish it from the standard form. |
SLANG-GraphRAG: Multi-Layered Retrieval with Domain-Specific Knowledge for Low Resource Social Media Conversations (2026.findings-eacl)
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| Challenge: | Standard NLP benchmarks often miss subtle, culturally-specific cues in social media . incorporating structured cultural knowledge into the retrieval process improves accuracy by up to 31% . |
| Approach: | They propose a retrieval-augmented framework that integrates a culture-specific slang knowledge graph into large language models via one-shot prompting. |
| Outcome: | The proposed framework outperforms traditional and unstructured retrieval methods in slang-based models by 31% and 28%. |
Understanding Slang with LLMs: Modelling Cross-Cultural Nuances through Paraphrasing (2024.emnlp-main)
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| Challenge: | a recent study examines the ability of large language models (LLMs) to paraphrase slang within climate-related tweets . slanted tweets from non-anglocentric countries may contain cultural references and idioms based on sociocultural identities . |
| Approach: | They investigate the ability of large language models to paraphrase slang within climate-related tweets from Nigeria and the UK. |
| Outcome: | The proposed model can paraphrase slang within climate-related tweets from Nigeria and the UK . the model can only parse sexist and sex-related slurs, the study shows . |
Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang (2022.acl-long)
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| Challenge: | a recent study suggests that language evolution is a diachronic process, but no causal analysis is performed to verify these claims. |
| Approach: | They analyze the semantic change and frequency shift of slang words and compare them to those of standard, nonslang terms. |
| Outcome: | The proposed model shows that slang has smaller semantic change but larger frequency shifts over time. |
Why Swear? Analyzing and Inferring the Intentions of Vulgar Expressions (D18-1)
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| Challenge: | Vulgar words are employed in language use for several different functions, including expressing aggression, signaling group identity or the informality of the communication. |
| Approach: | They present a dataset of 7,800 tweets with six categories of vulgarity in which all instances of vulgar words are annotated with one of the six categories. |
| Outcome: | The proposed model can predict the category of a vulgar word based on the immediate context it appears in with 67.4 macro F1 across six classes. |