# A Computational Framework for Slang Generation

arXiv: https://arxiv.org/abs/2102.01826 · HTML: https://mlsanity.com/paper/2102.01826/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 76 references that are in the map.
- First posted on arXiv 3 Feb 2021.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change](https://mlsanity.com/paper/1605.09096/) (2016)
- [Demographic Dialectal Variation in Social Media: A Case Study of African-American English](https://mlsanity.com/paper/1608.08868/) (2016)
- [Learning Fine-grained Image Similarity with Deep Ranking](https://mlsanity.com/paper/1404.4661/) (2014)
- [Enabling Language Models to Fill in the Blanks](https://mlsanity.com/paper/2005.05339/) (2020)
- [Neural Metaphor Detection in Context](https://mlsanity.com/paper/1808.09653/) (2018)
- [Deep convolutional acoustic word embeddings using word-pair side information](https://mlsanity.com/paper/1510.01032/) (2015)
- [Learning to Explain Non-Standard English Words and Phrases](https://mlsanity.com/paper/1709.09254/) (2017)
- [Making "fetch" happen: The influence of social and linguistic context on nonstandard word growth and decline](https://mlsanity.com/paper/1802.04140/) (2018)
- [Simple Models for Word Formation in English Slang](https://mlsanity.com/paper/1804.02596/) (2018)

## Built on by (most cited first, 11 of 11)

- [SLANG: New Concept Comprehension of Large Language Models](https://mlsanity.com/paper/2401.12585/) (2024)
- [Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang](https://mlsanity.com/paper/2203.04651/) (2022)
- [Toward Informal Language Processing: Knowledge of Slang in Large Language Models](https://mlsanity.com/paper/2404.02323/) (2024)
- [Semantically Informed Slang Interpretation](https://mlsanity.com/paper/2205.00616/) (2022)
- [Tracing Semantic Variation in Slang](https://mlsanity.com/paper/2210.08635/) (2022)
- [Word sense extension](https://mlsanity.com/paper/2306.05609/) (2023)
- [ChainNet: Structured Metaphor and Metonymy in WordNet](https://mlsanity.com/paper/2403.20308/) (2024)
- [FeelsGoodMan: Inferring Semantics of Twitch Neologisms](https://mlsanity.com/paper/2108.08411/) (2021)
- [Systematic word meta-sense extension](https://mlsanity.com/paper/2311.13029/) (2023)
- [How do Language Models Generate Slang: A Systematic Comparison between Human and Machine-Generated Slang Usages](https://mlsanity.com/paper/2509.15518/) (2025)
- [slang.gr as a Large-Scale Crowdsourced Resource for Non-Standard Greek](https://mlsanity.com/paper/2607.21255/) (2026)

## Abstract (arXiv)

Slang is a common type of informal language, but its flexible nature and paucity of data resources present challenges for existing natural language systems. We take an initial step toward machine generation of slang by developing a framework that models the speaker's word choice in slang context. Our framework encodes novel slang meaning by relating the conventional and slang senses of a word while incorporating syntactic and contextual knowledge in slang usage. We construct the framework using a combination of probabilistic inference and neural contrastive learning. We perform rigorous evaluations on three slang dictionaries and show that our approach not only outperforms state-of-the-art language models, but also better predicts the historical emergence of slang word usages from 1960s to 2000s. We interpret the proposed models and find that the contrastively learned semantic space is sensitive to the similarities between slang and conventional senses of words. Our work creates opportunities for the automated generation and interpretation of informal language.