Adapting Definition Modeling for New Languages: A Case Study on Belarusian
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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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 26 references that are in the map. First posted on arXiv 13 Jul 2025.
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References · 11
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
- 2018A Call for Clarity in Reporting BLEU Scorescited by 1,790
- 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,196
- 2021Learning Compact Metrics for MTcited by 93
- 2016Definition Modeling: Learning to define word embeddings in natural languagecited by 46
- 2018Conditional Generators of Words Definitionscited by 35
- 2015Learning to Understand Phrases by Embedding the Dictionarycited by 33
- 2021Understanding Jargon: Combining Extraction and Generation for Definition Modelingcited by 15
- 2023"Definition Modeling: To model definitions." Generating Definitions With Little to No Semanticscited by 4
- 2024DORE: A Dataset For Portuguese Definition Generationcited by 2
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