# BERT for Monolingual and Cross-Lingual Reverse Dictionary

arXiv: https://arxiv.org/abs/2009.14790 · HTML: https://mlsanity.com/paper/2009.14790/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2020.

## Built on by (most cited first, 12 of 12)

- [Semeval-2022 Task 1: CODWOE -- Comparing Dictionaries and Word Embeddings](https://mlsanity.com/paper/2205.13858/) (2022)
- [On the Tip of the Tongue: Analyzing Conceptual Representation in Large Language Models with Reverse-Dictionary Probe](https://mlsanity.com/paper/2402.14404/) (2024)
- [A Unified Model for Reverse Dictionary and Definition Modelling](https://mlsanity.com/paper/2205.04602/) (2022)
- [WordAlchemy: A transformer-based Reverse Dictionary](https://mlsanity.com/paper/2204.10181/) (2022)
- [CuRIAM: Corpus re Interpretation and Metalanguage in U.S. Supreme Court Opinions](https://mlsanity.com/paper/2305.14719/) (2023)
- [S~onajaht: Definition Embeddings and Semantic Search for Reverse Dictionary Creation](https://mlsanity.com/paper/2404.19430/) (2024)
- [IRB-NLP at SemEval-2022 Task 1: Exploring the Relationship Between Words and Their Semantic Representations](https://mlsanity.com/paper/2205.06840/) (2022)
- [Advancing Arabic Reverse Dictionary Systems: A Transformer-Based Approach with Dataset Construction Guidelines](https://mlsanity.com/paper/2504.21475/) (2025)
- [Rosetta Stone at KSAA-RD Shared Task: A Hop From Language Modeling To Word--Definition Alignment](https://mlsanity.com/paper/2310.15823/) (2023)
- [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)
- [MURAD: A Large-Scale Multi-Domain Unified Reverse Arabic Dictionary Dataset](https://mlsanity.com/paper/2601.21512/) (2026)

## Abstract (arXiv)

Reverse dictionary is the task to find the proper target word given the word description. In this paper, we tried to incorporate BERT into this task. However, since BERT is based on the byte-pair-encoding (BPE) subword encoding, it is nontrivial to make BERT generate a word given the description. We propose a simple but effective method to make BERT generate the target word for this specific task. Besides, the cross-lingual reverse dictionary is the task to find the proper target word described in another language. Previous models have to keep two different word embeddings and learn to align these embeddings. Nevertheless, by using the Multilingual BERT (mBERT), we can efficiently conduct the cross-lingual reverse dictionary with one subword embedding, and the alignment between languages is not necessary. More importantly, mBERT can achieve remarkable cross-lingual reverse dictionary performance even without the parallel corpus, which means it can conduct the cross-lingual reverse dictionary with only corresponding monolingual data. Code is publicly available at https://github.com/yhcc/BertForRD.git.