conference-paper
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AutoText: An End-to-End AutoAI Framework for Text
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- Citations
- 5
- References
- 26
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Paper overview
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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Publication details
- DOI
- 10.1609/aaai.v35i18.17993
- OpenAlex
- W3174337589
- Document type
- conference-paper
- Language
- EN
- Source
- Proceedings of the AAAI Conference on Artificial Intelligence
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