AutoNLU：一个基于云的企业自然语言理解系统
AutoNLU: An On-demand Cloud-based Natural Language Understanding System for Enterprises
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）任务上取得了可喜的成果。即使许多神经网络模型的源代码是公开的，但从开源模型到解决企业中的现实问题仍然存在很大差距。因此，为了填补这一空白，我们引入了AutoNLU，这是一个基于云的按需系统，具有易于使用的界面，涵盖了开发NLU模型的所有常见用例和步骤。AutoNLU为Adobe内部的许多产品团队提供了不同的用例和数据集，并快速为他们提供了工作模型。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）我们构建了强大的关键词提取模型，在两个公共基准上实现了最先进的结果。在这两种情况下，最终用户只需要编写少量代码就可以将数据集转换为AutoNLU使用的通用格式。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
