论文标题
通过FASL活跃的几次学习
Active Few-Shot Learning with FASL
论文作者
论文摘要
自然语言处理（NLP）的最新进展已导致许多任务的强大文本分类模型。但是，以高质量培训模型，仍然需要数千个示例。这使得针对现实世界中的问题和业务需求快速开发和部署新模型变得具有挑战性。很少有学习和积极学习是两条研究线，旨在解决这个问题。在这项工作中，我们将这两条线结合到FASL，该平台允许使用迭代和快速过程培训文本分类模型。我们研究哪种主动学习方法在我们的几次设置中最有效。此外，我们开发了一个模型来预测何时停止注释。这很重要，因为在几次设置中，我们无法访问大型验证集。
Recent advances in natural language processing (NLP) have led to strong text classification models for many tasks. However, still often thousands of examples are needed to train models with good quality. This makes it challenging to quickly develop and deploy new models for real world problems and business needs. Few-shot learning and active learning are two lines of research, aimed at tackling this problem. In this work, we combine both lines into FASL, a platform that allows training text classification models using an iterative and fast process. We investigate which active learning methods work best in our few-shot setup. Additionally, we develop a model to predict when to stop annotating. This is relevant as in a few-shot setup we do not have access to a large validation set.
