17993@AAAI

#1 AutoText: An End-to-End AutoAI Framework for Text [PDF¹] [Copy] [Kimi] [REL]

Authors: Arunima Chaudhary, Alayt Issak, Kiran Kate, Yannis Katsis, Abel Valente, Dakuo Wang, Alexandre Evfimievski, Sairam Gurajada, Ban Kawas, Cristiano Malossi, Lucian Popa, Tejaswini Pedapati, Horst Samulowitz, Martin Wistuba, Yunyao Li

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.