论文标题
低资源语言数据集创建，策展和分类：Setswana和Sepedi-扩展摘要
Low resource language dataset creation, curation and classification: Setswana and Sepedi -- Extended Abstract
论文作者
论文摘要
自然语言处理的最新进展仅是代表良好的语言的福音，否定了鲜为人知的全球语言的研究。这部分是由于精选的数据和研究资源的可用性。有关低资源语言的当前挑战之一是关于不同用例的数据集的收集，策划和准备的明确指南。在这项工作中，我们执行的任务是创建两个专注于Setswana和Sepedi的新闻头条（即短文）以及从这些数据集创建新闻主题分类任务的任务。在这项研究中，我们记录了我们的工作，提出了用于分类的基准，并调查了一种更适合低资源语言的数据增强方法，以提高分类器的性能。
The recent advances in Natural Language Processing have only been a boon for well represented languages, negating research in lesser known global languages. This is in part due to the availability of curated data and research resources. One of the current challenges concerning low-resourced languages are clear guidelines on the collection, curation and preparation of datasets for different use-cases. In this work, we take on the task of creating two datasets that are focused on news headlines (i.e short text) for Setswana and Sepedi and the creation of a news topic classification task from these datasets. In this study, we document our work, propose baselines for classification, and investigate an approach on data augmentation better suited to low-resourced languages in order to improve the performance of the classifiers.
