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

Authors

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

DOI:

https://doi.org/10.1609/aaai.v35i18.17993

Keywords:

AutoAI, AutoML, CASH, Text Classification

Abstract

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.

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Published

2021-05-18

How to Cite

Chaudhary, A., Issak, A., Kate, K., Katsis, Y., Valente, A., Wang, D., … 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