AAAI Conference 2021 System Paper
AutoText: An End-to-End AutoAI Framework for Text
- Arunima Chaudhary
- Alayt Issak
- Kiran Kate
- Yannis Katsis
- Abel Valente
- Dakuo Wang
- Alexandre Evfimievski
- Sairam Gurajada
Building models for natural language processing (NLP) tasks remains a daunting task for many, requiring significant technical expertise, efforts, and resources. In this demonstration, we present AutoText, an end-to-end AutoAI framework for text, to lower the barrier of entry in building NLP models. AutoText combines state-of-the-art AutoAI optimization techniques and learning algorithms for NLP tasks into a single extensible framework. Through its simple, yet powerful UI, non-AI experts (e. g. , domain experts) can quickly generate performant NLP models with support to both control (e. g. , via specifying constraints) and understand learned models.