构建多语言职业实体链接评估基准，覆盖21种语言48个数据集。
MELO: An Evaluation Benchmark for Multilingual Entity Linking of Occupations
- 基于人类标注数据构建21语言职业实体链接基准
- 在零样本设置下测试简单模型与通用编码器表现
- 公开数据集与代码，支持标准化评估
我们提出了多语言职业实体链接评估基准（MELO），包含48个数据集，用于评估21种语言中实体提及链接到ESCO职业多语言分类体系的性能。MELO基于高质量、已有的人工标注数据构建。我们采用简单的词汇模型和通用句子编码器，在零样本设置下作为双编码器进行实验，为未来研究建立基线。数据集及源代码已公开于https://github.com/Avature/melo-benchmark，支持标准化评估。
原文摘要 · Abstract (English)
We present the Multilingual Entity Linking of Occupations (MELO) Benchmark, a new collection of 48 datasets for evaluating the linking of entity mentions in 21 languages to the ESCO Occupations multilingual taxonomy. MELO was built using high-quality, pre-existent human annotations. We conduct experiments with simple lexical models and general-purpose sentence encoders, evaluated as bi-encoders in a zero-shot setup, to establish baselines for future research. The datasets and source code for standardized evaluation are publicly available at https://github.com/Avature/melo-benchmark
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