# Adapting Definition Modeling for New Languages: A Case Study on Belarusian

arXiv: https://arxiv.org/abs/2507.09536 · HTML: https://mlsanity.com/paper/2507.09536/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 13 Jul 2025.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [Learning Compact Metrics for MT](https://mlsanity.com/paper/2110.06341/) (2021)
- [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)
- [Understanding Jargon: Combining Extraction and Generation for Definition Modeling](https://mlsanity.com/paper/2111.07267/) (2021)
- ["Definition Modeling: To model definitions." Generating Definitions With Little to No Semantics](https://mlsanity.com/paper/2306.08433/) (2023)
- [DORE: A Dataset For Portuguese Definition Generation](https://mlsanity.com/paper/2403.18018/) (2024)

## Abstract (arXiv)

Definition modeling, the task of generating new definitions for words in context, holds great prospect as a means to assist the work of lexicographers in documenting a broader variety of lects and languages, yet much remains to be done in order to assess how we can leverage pre-existing models for as-of-yet unsupported languages. In this work, we focus on adapting existing models to Belarusian, for which we propose a novel dataset of 43,150 definitions. Our experiments demonstrate that adapting a definition modeling systems requires minimal amounts of data, but that there currently are gaps in what automatic metrics do capture.