AutoText: An End-to-End AutoAI Framework for Text

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.

Authors

• Arunima Chaudhary
• Alayt Issak
• Kiran Kate IBM Research
• Yannis Katsis
• Abel Valente
• Dakuo Wang
• Alexandre Evfimievski
• Sairam Gurajada
• Ban Kawas
• Cristiano Malossi
• Lucian Popa IBM Almaden Research Center
• Tejaswini Pedapati IBM Research
• Horst Samulowitz IBM Research
• Martin Wistuba
• Yunyao Li Adobe

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