论文标题
Autonlu：企业的基于按需的基于云的自然语言理解系统
AutoNLU: An On-demand Cloud-based Natural Language Understanding System for Enterprises
论文作者
论文摘要
随着深度学习的复兴，神经网络在许多自然语言理解（NLU）任务上取得了令人鼓舞的结果。即使许多神经网络模型的源代码都可以公开使用，但从开源模型到解决企业中的现实世界问题的差距仍然很大。因此，为了填补这一空白，我们引入了Autonlu，这是一种基于点播的基于云的系统，具有易于使用的接口，涵盖了所有常见的用例和开发NLU模型的步骤。 Autonlu支持了Adobe中的许多产品团队，其中包括不同的用例和数据集，并迅速提供了工作模型。为了证明Autonlu的有效性，我们提出了两个案例研究。 i）我们建立了一个实用的NLU模型，用于处理Photoshop中的各种图像编辑请求。 ii）我们构建了强大的钥匙拼式提取模型，以在两个公共基准上实现最新的结果。在这两种情况下，最终用户只需要编写少量代码即可将其数据集转换为Autonlu使用的通用格式。
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.
