Abstract
Open-set learning and discovery (OSLD) is a challenging machine learning task in which samples from new (unknown) classes can appear at test time. It can be seen as a generalization of zero-shot learning, where the new classes are not known a priori, hence involving the active discovery of new classes. While zero-shot learning has been extensively studied in text classification, especially with the emergence of pre-trained language models, open-set learning and discovery is a comparatively new setup for the text domain. To this end, we introduce the first multilingual open-set learning and discovery (MOSLD) benchmark for text categorization by topic, comprising 960K data samples across 12 languages. To construct the benchmark, we (i) rearrange existing datasets and (ii) collect new data samples from the news domain. Moreover, we propose a novel framework for the OSLD task, which integrates multiple stages to continuously discover and learn new classes. We evaluate several language models, including our own, to obtain results that can be used as reference for future work. We release our benchmark at this https URL.
Abstract (translated)
开放集学习与发现（OSLD）是一项具有挑战性的机器学习任务，在该任务中，测试时可能出现来自新类别的样本。这可以被视为零样本学习的一种泛化形式，其中新的类别事先未知，因此需要主动发现新类别。虽然在文本分类领域特别是在预训练语言模型出现之后对零样本学习进行了广泛的研究，但开放集学习和发现对于文本领域来说是一个相对较新的设置。 为此，我们引入了一个名为多语言开放集学习与发现（MOSLD）的基准测试，用于按主题进行文本分类，包含12种语言共96万个数据样本。为了构建该基准测试，我们(i)重新组织现有的数据集和(ii)从新闻领域收集新的数据样本。 此外，我们还提出了一种新颖的框架来处理OSLD任务，该框架整合了多个阶段以持续发现并学习新类别。我们评估了几种语言模型（包括我们自己的），从而获得未来研究可以参考的结果。 我们的基准测试可以在以下网址获取：[请在此处提供链接]。
URL
https://arxiv.org/abs/2601.13437
