论文标题
方位角：文本分类的系统错误分析
Azimuth: Systematic Error Analysis for Text Classification
论文作者
论文摘要
我们提出了Azimuth，这是一种开源且易于使用的工具，用于对文本分类进行错误分析。与ML开发周期的其他阶段相比，例如模型训练和高参数调整，误差分析阶段的过程和工具不那么成熟。但是，此阶段对于开发可靠且值得信赖的AI系统至关重要。为了使错误分析更加系统，我们提出了一种包含数据集分析和模型质量评估的方法，这是方位表促进的。我们旨在帮助AI从业者发现并解决模型不会通过利用和集成一系列ML技术（例如显着图，相似性，不确定性和行为分析）来概括的领域。我们的代码和文档可在github.com/servicenow/azimuth上找到。
We present Azimuth, an open-source and easy-to-use tool to perform error analysis for text classification. Compared to other stages of the ML development cycle, such as model training and hyper-parameter tuning, the process and tooling for the error analysis stage are less mature. However, this stage is critical for the development of reliable and trustworthy AI systems. To make error analysis more systematic, we propose an approach comprising dataset analysis and model quality assessment, which Azimuth facilitates. We aim to help AI practitioners discover and address areas where the model does not generalize by leveraging and integrating a range of ML techniques, such as saliency maps, similarity, uncertainty, and behavioral analyses, all in one tool. Our code and documentation are available at github.com/servicenow/azimuth.
