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.

翻译：随着深层学习的复兴,神经网络在许多自然语言理解(NLU)任务方面取得了令人乐观的成果。尽管许多神经网络模型的源代码是公开的,但从开放源码模型到解决企业中现实世界问题之间仍然存在着巨大的差距。因此,为了填补这一差距,我们引入了AutoNLU, 即时需求云层系统,它是一个容易使用的云层界面,涵盖所有通用使用案例和开发NLU模型的步骤。AutoNLU支持Adobe内部的许多产品团队,使用不同的使用案例和数据集,迅速交付它们的工作模型。为了展示AutoNLU的有效性,我们介绍了两个案例研究。 (i) 我们建立一个实用的NLU模型,用于处理摄影店中的各种图像编辑请求。 (ii) 我们根据两个公共基准构建了强大的关键词提取模型,能够实现最新结果。在这两种情况下,终端用户只需写少量代码,即可将其数据集转换成AutoNLU使用的共同格式。