AutoText: An End-to-End AutoAI Framework for Text
DOI:
https://doi.org/10.1609/aaai.v35i18.17993Keywords:
AutoAI, AutoML, CASH, Text ClassificationAbstract
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.Downloads
Published
2021-05-18
How to Cite
Chaudhary, A., Issak, A., Kate, K., Katsis, Y., Valente, A., Wang, D., Evfimievski, A., Gurajada, S., Kawas, B., Malossi, C., Popa, L., Pedapati, T., Samulowitz, H., Wistuba, M., & Li, Y. (2021). AutoText: An End-to-End AutoAI Framework for Text. Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 16001-16003. https://doi.org/10.1609/aaai.v35i18.17993
Issue
Section
AAAI Demonstration Track