It was always tempting to start an explanatory effort. A team of researchers introduced The Portable Text Annotation tool (POTATO), a web-based application approved for use in the EMNLP 2022 DEMO track. The Potato Project Center is designed to make it easy to replicate existing annotation efforts.
Potato facilitates rapid prototyping and deployment of multiple text annotation tasks. This work aims to enable individuals or small groups to annotate textual information with minimal effort, starting from scratch and completing an annotation with a few lines of configuration. Annotators use a web-based front-end to work with data, while a potato back-end can be started locally as a web server.
A single configuration file determines the functions and data types used by Potato. Users don’t need to know how to code to get started with Potato. Potato is customizable, allowing users to modify the user interface and allow their descriptors to interact without the need for additional web design. Users can quickly retrieve a project with a potato and open the annotation area.
The variety of annotation tools supported by Potato is amazing.
- Easy to set up and adaptable to a variety of requirements: Changing potato settings is as easy as editing a file. Creating an explanatory website does not involve coding. Like other features, Potato offers a wide range of customization options.
- Predefined configurations and defaults: Annotation patterns like radio, like, checkbox, textbox, span, pairwise comparison, worst-size, image/video-as-label, etc. are all supported by Potato.
- Multiple Data Formats: Potato can display anything from short documents to long conversations, comparisons, and more.
- Researchers in natural language processing (NLP) may need to perform a battery of related but distinct tasks (eg, multilingual annotation). Potato supports multi-language Twitter Intimacy Analysis functionality, which makes it possible to build configuration files for all functions with minimal effort.
- Increase efficiency in the explanation; Potato was designed with many features to improve the experience of animators and render annotations faster.
- Keyboard shortcuts are easy to set up: keyboard shortcuts allow you to enter descriptive responses quickly and easily.
- Dynamically highlighting possible relationships between names and keywords in the document can be dynamically highlighted, which can be set up for tasks with multiple tags or very long documents.
- With so many tags, it can be difficult for descriptors to track their meaning without the help of tooltips. Thanks to Potato’s customizable label tools, illustrators can learn more about labels by hovering their mouse over them.
- improving the knowledge of pilots; Potato provides tools to learn more about annotators who have worked on user data and to identify any potential biases. Potato’s user-friendly interface makes it easy to create both pre- and post-screening queries, which can shed light on users’ descriptive professional histories. Potato includes question templates that make it easy to develop standard competency questionnaires such as demographics.
- improving quality assurance; Potato includes tools to identify spammers and collect more honest comments.
- Potato’s attention test feature makes it easy to create questions designed to find spammers and randomly put them into the explanation queue.
- Users can quickly and easily identify ineligible descriptors using Potato’s built-in qualification check before proceeding with full data identification.
- With Potato’s built-in time tracking, illustrators can easily track how much time they spend on each example and gain insight into their work habits.
Since Potato is hosted on pypi, users can simply run “pip install potato-annotation” to get it up and running. Potatoes can be easily deployed online to collect annotations from popular aggregator platforms like Prolifc.com. Users need a server with accessible ports to use Potato in a crowded environment. Potato works seamlessly with Prolific, a platform for finding and hiring interns.
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Tanushree Shenwai is an intern at MarketingPost Consulting. She is currently pursuing her B.Tech from Indian Institute of Technology (IIT), Bhubaneswar. She is a data science enthusiast and has a keen interest in the application of artificial intelligence in various fields. She is passionate about exploring new developments in technology and their real-life applications.
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