# Learning to Explain Non-Standard English Words and Phrases

arXiv: https://arxiv.org/abs/1709.09254 · HTML: https://mlsanity.com/paper/1709.09254/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2017.

## Built on by (most cited first, 21 of 21)

- [Self-Supervised Euphemism Detection and Identification for Content Moderation](https://mlsanity.com/paper/2103.16808/) (2021)
- [Interpretable Word Sense Representations via Definition Generation: The Case of Semantic Change Analysis](https://mlsanity.com/paper/2305.11993/) (2023)
- [Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang](https://mlsanity.com/paper/2203.04651/) (2022)
- [Assisting Language Learners: Automated Trans-Lingual Definition Generation via Contrastive Prompt Learning](https://mlsanity.com/paper/2306.06058/) (2023)
- [A Computational Framework for Slang Generation](https://mlsanity.com/paper/2102.01826/) (2021)
- [Fine-grained Contrastive Learning for Definition Generation](https://mlsanity.com/paper/2210.00543/) (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)
- [Semantic Role Labeling Meets Definition Modeling: Using Natural Language to Describe Predicate-Argument Structures](https://mlsanity.com/paper/2212.01094/) (2022)
- ["Definition Modeling: To model definitions." Generating Definitions With Little to No Semantics](https://mlsanity.com/paper/2306.08433/) (2023)
- [Tracing Semantic Variation in Slang](https://mlsanity.com/paper/2210.08635/) (2022)
- [SlangDIT: Benchmarking LLMs in Interpretative Slang Translation](https://mlsanity.com/paper/2505.14181/) (2025)
- [DORE: A Dataset For Portuguese Definition Generation](https://mlsanity.com/paper/2403.18018/) (2024)
- [Exploiting Correlations Between Contexts and Definitions with Multiple Definition Modeling](https://mlsanity.com/paper/2305.14717/) (2023)
- [3D-EX : A Unified Dataset of Definitions and Dictionary Examples](https://mlsanity.com/paper/2308.03043/) (2023)
- [Towards Automated Lexicography: Generating and Evaluating Definitions for Learner's Dictionaries](https://mlsanity.com/paper/2601.01842/) (2026)
- [Systematic word meta-sense extension](https://mlsanity.com/paper/2311.13029/) (2023)
- [SLM4Offer: Personalized Marketing Offer Generation Using Contrastive Learning Based Fine-Tuning](https://mlsanity.com/paper/2508.15471/) (2025)
- [How do Language Models Generate Slang: A Systematic Comparison between Human and Machine-Generated Slang Usages](https://mlsanity.com/paper/2509.15518/) (2025)
- [LM-Lexicon: Improving Definition Modeling via Harmonizing Semantic Experts](https://mlsanity.com/paper/2602.14060/) (2026)
- [Slang Context-based Inference Enhancement via Greedy Search-Guided Chain-of-Thought Prompting](https://mlsanity.com/paper/2603.13230/) (2026)

## Abstract (arXiv)

We describe a data-driven approach for automatically explaining new, non-standard English expressions in a given sentence, building on a large dataset that includes 15 years of crowdsourced examples from UrbanDictionary.com. Unlike prior studies that focus on matching keywords from a slang dictionary, we investigate the possibility of learning a neural sequence-to-sequence model that generates explanations of unseen non-standard English expressions given context. We propose a dual encoder approach---a word-level encoder learns the representation of context, and a second character-level encoder to learn the hidden representation of the target non-standard expression. Our model can produce reasonable definitions of new non-standard English expressions given their context with certain confidence.