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.
CITATION STYLE
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
Mendeley helps you to discover research relevant for your work.