# A Unified Model for Reverse Dictionary and Definition Modelling

arXiv: https://arxiv.org/abs/2205.04602 · HTML: https://mlsanity.com/paper/2205.04602/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 26 references that are in the map.
- First posted on arXiv 9 May 2022.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [Using the Output Embedding to Improve Language Models](https://mlsanity.com/paper/1608.05859/) (2016)
- [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)
- [Multi-channel Reverse Dictionary Model](https://mlsanity.com/paper/1912.08441/) (2019)
- [Mark my Word: A Sequence-to-Sequence Approach to Definition Modeling](https://mlsanity.com/paper/1911.05715/) (2019)
- [Mapping Text to Knowledge Graph Entities using Multi-Sense LSTMs](https://mlsanity.com/paper/1808.07724/) (2018)
- [BERT for Monolingual and Cross-Lingual Reverse Dictionary](https://mlsanity.com/paper/2009.14790/) (2020)
- [xSense: Learning Sense-Separated Sparse Representations and Textual Definitions for Explainable Word Sense Networks](https://mlsanity.com/paper/1809.03348/) (2018)

## Built on by (most cited first, 5 of 5)

- [CoLLEGe: Concept Embedding Generation for Large Language Models](https://mlsanity.com/paper/2403.15362/) (2024)
- [Advancing Arabic Reverse Dictionary Systems: A Transformer-Based Approach with Dataset Construction Guidelines](https://mlsanity.com/paper/2504.21475/) (2025)
- [Multilingual Definition Modeling](https://mlsanity.com/paper/2506.01489/) (2025)
- [Towards a Transformer-Based Reverse Dictionary Model for Quality Estimation of Definitions](https://mlsanity.com/paper/2311.02985/) (2023)
- [GEAR: A Simple GENERATE, EMBED, AVERAGE AND RANK Approach for Unsupervised Reverse Dictionary](https://mlsanity.com/paper/2412.06654/) (2024)

## Abstract (arXiv)

We build a dual-way neural dictionary to retrieve words given definitions, and produce definitions for queried words. The model learns the two tasks simultaneously and handles unknown words via embeddings. It casts a word or a definition to the same representation space through a shared layer, then generates the other form in a multi-task fashion. Our method achieves promising automatic scores on previous benchmarks without extra resources. Human annotators prefer the model's outputs in both reference-less and reference-based evaluation, indicating its practicality. Analysis suggests that multiple objectives benefit learning.