# 3D-EX : A Unified Dataset of Definitions and Dictionary Examples

arXiv: https://arxiv.org/abs/2308.03043 · HTML: https://mlsanity.com/paper/2308.03043/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 79 references that are in the map.
- First posted on arXiv 6 Aug 2023.

## Builds on (most cited first, 25 of 25)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [ELI5: Long Form Question Answering](https://mlsanity.com/paper/1907.09190/) (2019)
- [WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations](https://mlsanity.com/paper/1808.09121/) (2018)
- [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://mlsanity.com/paper/2212.09741/) (2022)
- [A Question-Entailment Approach to Question Answering](https://mlsanity.com/paper/1901.08079/) (2019)
- [Definition Modeling: Learning to define word embeddings in natural language](https://mlsanity.com/paper/1612.00394/) (2016)
- [Conditional Generators of Words Definitions](https://mlsanity.com/paper/1806.10090/) (2018)
- [Learning to Understand Phrases by Embedding the Dictionary](https://mlsanity.com/paper/1504.00548/) (2015)
- [HyperLex: A Large-Scale Evaluation of Graded Lexical Entailment](https://mlsanity.com/paper/1608.02117/) (2016)
- [Dictionary-based Debiasing of Pre-trained Word Embeddings](https://mlsanity.com/paper/2101.09525/) (2021)
- [Learning to Explain Non-Standard English Words and Phrases](https://mlsanity.com/paper/1709.09254/) (2017)
- [Semeval-2022 Task 1: CODWOE -- Comparing Dictionaries and Word Embeddings](https://mlsanity.com/paper/2205.13858/) (2022)
- [Contextualized Representations Using Textual Encyclopedic Knowledge](https://mlsanity.com/paper/2004.12006/) (2020)
- [Multi-channel Reverse Dictionary Model](https://mlsanity.com/paper/1912.08441/) (2019)
- [VCDM: Leveraging Variational Bi-encoding and Deep Contextualized Word Representations for Improved Definition Modeling](https://mlsanity.com/paper/2010.03124/) (2020)
- [SemEval-2020 Task 6: Definition extraction from free text with the DEFT corpus](https://mlsanity.com/paper/2008.13694/) (2020)
- [FEWS: Large-Scale, Low-Shot Word Sense Disambiguation with the Dictionary](https://mlsanity.com/paper/2102.07983/) (2021)
- [ALL Dolphins Are Intelligent and SOME Are Friendly: Probing BERT for Nouns' Semantic Properties and their Prototypicality](https://mlsanity.com/paper/2110.06376/) (2021)
- [DictBERT: Dictionary Description Knowledge Enhanced Language Model Pre-training via Contrastive Learning](https://mlsanity.com/paper/2208.00635/) (2022)
- [Implementing a Reverse Dictionary, based on word definitions, using a Node-Graph Architecture](https://mlsanity.com/paper/1606.00025/) (2016)
- [Multilingual Extraction and Categorization of Lexical Collocations with Graph-aware Transformers](https://mlsanity.com/paper/2205.11456/) (2022)
- [Spanish Built Factual Freectianary (Spanish-BFF): the first AI-generated free dictionary](https://mlsanity.com/paper/2302.12746/) (2023)
- [The Interplay between Lexical Resources and Natural Language Processing](https://mlsanity.com/paper/1807.00571/) (2018)

## Built on by (most cited first, 1 of 1)

- [MURAD: A Large-Scale Multi-Domain Unified Reverse Arabic Dictionary Dataset](https://mlsanity.com/paper/2601.21512/) (2026)

## Abstract (arXiv)

Definitions are a fundamental building block in lexicography, linguistics and computational semantics. In NLP, they have been used for retrofitting word embeddings or augmenting contextual representations in language models. However, lexical resources containing definitions exhibit a wide range of properties, which has implications in the behaviour of models trained and evaluated on them. In this paper, we introduce 3D- EX , a dataset that aims to fill this gap by combining well-known English resources into one centralized knowledge repository in the form of <term, definition, example> triples. 3D- EX is a unified evaluation framework with carefully pre-computed train/validation/test splits to prevent memorization. We report experimental results that suggest that this dataset could be effectively leveraged in downstream NLP tasks. Code and data are available at https://github.com/F-Almeman/3D-EX .