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

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.