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

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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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APA

Chaudhary, A., Issak, A., Kate, K., Katsis, Y., Valente, A., Wang, D., … Li, Y. (2021). AutoText: An End-to-End AutoAI Framework for Text. In 35th AAAI Conference on Artificial Intelligence, AAAI 2021 (Vol. 18, pp. 16001–16003). Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v35i18.17993

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