# Definition Modeling: Learning to define word embeddings in natural language

arXiv: https://arxiv.org/abs/1612.00394 · HTML: https://mlsanity.com/paper/1612.00394/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2016.

## Built on by (most cited first, 30 of 46)

- [Dict-BERT: Enhancing Language Model Pre-training with Dictionary](https://mlsanity.com/paper/2110.06490/) (2021)
- [SemEval-2024 Shared Task 6: SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakes](https://mlsanity.com/paper/2403.07726/) (2024)
- [Interpretable Word Sense Representations via Definition Generation: The Case of Semantic Change Analysis](https://mlsanity.com/paper/2305.11993/) (2023)
- [Multitasking Framework for Unsupervised Simple Definition Generation](https://mlsanity.com/paper/2203.12926/) (2022)
- [Semeval-2022 Task 1: CODWOE -- Comparing Dictionaries and Word Embeddings](https://mlsanity.com/paper/2205.13858/) (2022)
- [Understanding Jargon: Combining Extraction and Generation for Definition Modeling](https://mlsanity.com/paper/2111.07267/) (2021)
- [Assisting Language Learners: Automated Trans-Lingual Definition Generation via Contrastive Prompt Learning](https://mlsanity.com/paper/2306.06058/) (2023)
- [Learning Disentangled Representations for Natural Language Definitions](https://mlsanity.com/paper/2210.02898/) (2022)
- [Fine-grained Contrastive Learning for Definition Generation](https://mlsanity.com/paper/2210.00543/) (2022)
- [DEER: Descriptive Knowledge Graph for Explaining Entity Relationships](https://mlsanity.com/paper/2205.10479/) (2022)
- [A Unified Model for Reverse Dictionary and Definition Modelling](https://mlsanity.com/paper/2205.04602/) (2022)
- [Vision Meets Definitions: Unsupervised Visual Word Sense Disambiguation Incorporating Gloss Information](https://mlsanity.com/paper/2305.01788/) (2023)
- [Semantic Role Labeling Meets Definition Modeling: Using Natural Language to Describe Predicate-Argument Structures](https://mlsanity.com/paper/2212.01094/) (2022)
- [Multi-Relational Hyperbolic Word Embeddings from Natural Language Definitions](https://mlsanity.com/paper/2305.07303/) (2023)
- ["Definition Modeling: To model definitions." Generating Definitions With Little to No Semantics](https://mlsanity.com/paper/2306.08433/) (2023)
- [MALTO at SemEval-2024 Task 6: Leveraging Synthetic Data for LLM Hallucination Detection](https://mlsanity.com/paper/2403.00964/) (2024)
- [BLCU-ICALL at SemEval-2022 Task 1: Cross-Attention Multitasking Framework for Definition Modeling](https://mlsanity.com/paper/2204.07701/) (2022)
- [LlaMaVAE: Guiding Large Language Model Generation via Continuous Latent Sentence Spaces](https://mlsanity.com/paper/2312.13208/) (2023)
- [AXOLOTL'24 Shared Task on Multilingual Explainable Semantic Change Modeling](https://mlsanity.com/paper/2407.04079/) (2024)
- [SlangDIT: Benchmarking LLMs in Interpretative Slang Translation](https://mlsanity.com/paper/2505.14181/) (2025)
- [VER: Unifying Verbalizing Entities and Relations](https://mlsanity.com/paper/2211.11093/) (2022)
- [Spanish Built Factual Freectianary (Spanish-BFF): the first AI-generated free dictionary](https://mlsanity.com/paper/2302.12746/) (2023)
- [DORE: A Dataset For Portuguese Definition Generation](https://mlsanity.com/paper/2403.18018/) (2024)
- [Bridging Linguistic Structure and Mechanistic Interpretability for Conceptual Interpretation in Language Models](https://mlsanity.com/paper/2408.11827/) (2024)
- [Can Large Language Models Understand Internet Buzzwords Through User-Generated Content](https://mlsanity.com/paper/2505.15071/) (2025)
- [Simplifications are Absolutists: How Simplified Language Reduces Word Sense Awareness in LLM-Generated Definitions](https://mlsanity.com/paper/2507.11981/) (2025)
- [IRB-NLP at SemEval-2022 Task 1: Exploring the Relationship Between Words and Their Semantic Representations](https://mlsanity.com/paper/2205.06840/) (2022)
- [COMPILING: A Benchmark Dataset for Chinese Complexity Controllable Definition Generation](https://mlsanity.com/paper/2209.14614/) (2022)
- [Exploiting Correlations Between Contexts and Definitions with Multiple Definition Modeling](https://mlsanity.com/paper/2305.14717/) (2023)
- [Vec2Gloss: definition modeling leveraging contextualized vectors with Wordnet gloss](https://mlsanity.com/paper/2305.17855/) (2023)

## Abstract (arXiv)

Distributed representations of words have been shown to capture lexical semantics, as demonstrated by their effectiveness in word similarity and analogical relation tasks. But, these tasks only evaluate lexical semantics indirectly. In this paper, we study whether it is possible to utilize distributed representations to generate dictionary definitions of words, as a more direct and transparent representation of the embeddings' semantics. We introduce definition modeling, the task of generating a definition for a given word and its embedding. We present several definition model architectures based on recurrent neural networks, and experiment with the models over multiple data sets. Our results show that a model that controls dependencies between the word being defined and the definition words performs significantly better, and that a character-level convolution layer designed to leverage morphology can complement word-level embeddings. Finally, an error analysis suggests that the errors made by a definition model may provide insight into the shortcomings of word embeddings.