# Toward Cross-Lingual Definition Generation for Language Learners

arXiv: https://arxiv.org/abs/2010.05533 · HTML: https://mlsanity.com/paper/2010.05533/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2020.

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

- [Multitasking Framework for Unsupervised Simple Definition Generation](https://mlsanity.com/paper/2203.12926/) (2022)
- [BLCU-ICALL at SemEval-2022 Task 1: Cross-Attention Multitasking Framework for Definition Modeling](https://mlsanity.com/paper/2204.07701/) (2022)
- [COMPILING: A Benchmark Dataset for Chinese Complexity Controllable Definition Generation](https://mlsanity.com/paper/2209.14614/) (2022)
- [Multilingual Definition Modeling](https://mlsanity.com/paper/2506.01489/) (2025)
- [LitMind Dictionary: An Open-Source Online Dictionary](https://mlsanity.com/paper/2204.11087/) (2022)

## Abstract (arXiv)

Generating dictionary definitions automatically can prove useful for language learners. However, it's still a challenging task of cross-lingual definition generation. In this work, we propose to generate definitions in English for words in various languages. To achieve this, we present a simple yet effective approach based on publicly available pretrained language models. In this approach, models can be directly applied to other languages after trained on the English dataset. We demonstrate the effectiveness of this approach on zero-shot definition generation. Experiments and manual analyses on newly constructed datasets show that our models have a strong cross-lingual transfer ability and can generate fluent English definitions for Chinese words. We further measure the lexical complexity of generated and reference definitions. The results show that the generated definitions are much simpler, which is more suitable for language learners.