POTATO: The Portable Text Annotation Tool
This tool addresses annotation productivity for researchers and practitioners in ML/NLP, but it is incremental as it builds on existing annotation systems.
The authors tackled the problem of text annotation inefficiency by developing POTATO, a portable annotation tool that supports multimodal data and productivity features, resulting in improved labeling speed for long documents and complex tasks.
We present POTATO, the Portable text annotation tool, a free, fully open-sourced annotation system that 1) supports labeling many types of text and multimodal data; 2) offers easy-to-configure features to maximize the productivity of both deployers and annotators (convenient templates for common ML/NLP tasks, active learning, keypress shortcuts, keyword highlights, tooltips); and 3) supports a high degree of customization (editable UI, inserting pre-screening questions, attention and qualification tests). Experiments over two annotation tasks suggest that POTATO improves labeling speed through its specially-designed productivity features, especially for long documents and complex tasks. POTATO is available at https://github.com/davidjurgens/potato and will continue to be updated.