用少量数据让定义生成模型支持白俄罗斯语，验证了跨语言适配的可行性。
Adapting Definition Modeling for New Languages: A Case Study on Belarusian
- 基于4.3万条白俄语定义构建新数据集，支持模型迁移
- 仅需少量数据即可有效适配新语言，证明方法高效
- 现有自动评估指标仍不完善，适合词典编纂与低资源语言研究
定义建模旨在上下文中生成新词汇的定义，有助于词典学家记录更多方言和语言。然而，如何利用已有模型支持尚未覆盖的语言仍待探索。本文聚焦于将现有模型适配至白俄罗斯语，提出一个包含43,150条定义的新数据集。实验表明，定义建模系统的适配只需极少数据，但当前自动评估指标在捕捉性能方面仍存在不足。
原文摘要 · Abstract (English)
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.
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