Creoles represent an under-explored and marginalized group of languages, with few available resources for NLP research.While the genealogical ties between Creoles and a number of highly-resourced languages imply a significant potential for transfer learning, this potential is hampered due to this lack of annotated data. In this work we present CreoleVal, a collection of benchmark datasets spanning 8 different NLP tasks, covering up to 28 Creole languages; it is an aggregate of novel development datasets for reading comprehension, relation classification, and machine translation for Creoles, in addition to a practical gateway to a handful of preexisting benchmarks. For each benchmark, we conduct baseline experiments in a zero-shot setting in order to further ascertain the capabilities and limitations of transfer learning for Creoles. Ultimately, we see CreoleVal as an opportunity to empower research on Creoles in NLP and computational linguistics, and in general, a step towards more equitable language technology around the globe.

翻译：克里奥尔语是一组研究不足且被边缘化的语言，目前用于自然语言处理（NLP）研究的资源极为匮乏。尽管克里奥尔语与多种高资源语言之间存在谱系联系，这种迁移学习的潜力巨大，但由于缺乏标注数据而受到阻碍。本文提出CreoleVal——一个涵盖8种不同NLP任务的基准数据集集合，覆盖多达28种克里奥尔语；它整合了针对克里奥尔语的阅读理解、关系分类与机器翻译的新颖开发数据集，同时为若干现有基准提供了实用访问入口。针对每个基准任务，我们在零样本设置下开展基线实验，以进一步验证迁移学习在克里奥尔语上的能力与局限性。最终，我们将CreoleVal视为推动克里奥尔语在NLP与计算语言学领域研究的契机，并普遍视为迈向全球更公平语言技术的一步。