Papers with Slang

3 papers
A Computational Framework for Slang Generation (2021.tacl-1)

Copied to clipboard

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.
How do Language Models Generate Slang: A Systematic Comparison between Human and Machine-Generated Slang Usages (2025.findings-emnlp)

Copied to clipboard

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 .
Semantically Informed Slang Interpretation (2022.naacl-main)

Copied to clipboard

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.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations