With the renaissance of deep learning, neural networks have achieved
promising results on many natural language understanding (NLU) tasks. Even
though the source codes of many neural network models are publicly available,
there is still a large gap from open-sourced models to solving real-world
problems in enterprises. Therefore, to fill this gap, we introduce AutoNLU, an
on-demand cloud-based system with an easy-to-use interface that covers all
common use-cases and steps in developing an NLU model. AutoNLU has supported
many product teams within Adobe with different use-cases and datasets, quickly
delivering them working models. To demonstrate the effectiveness of AutoNLU, we
present two case studies. i) We build a practical NLU model for handling
various image-editing requests in Photoshop. ii) We build powerful keyphrase
extraction models that achieve state-of-the-art results on two public
benchmarks. In both cases, end users only need to write a small amount of code
to convert their datasets into a common format used by AutoNLU.

本文介绍 AutoNLU - 一种基于云的按需系统，通过易于使用的界面解决了开源神经网络模型与实际工业问题之间的巨大鸿沟，并展示了两个应用案例：一种处理 Photoshop 图像编辑请求的实用自然语言理解模型，以及实现两个公共基准测试最先进结果的强大的关键词提取模型。