论文标题
评估序列到序列学习模型
Evaluating Sequence-to-Sequence Learning Models for If-Then Program Synthesis
论文作者
论文摘要
实施企业流程自动化通常需要大量的技术专业知识和工程工作。对于非技术用户来说，能够用自然语言描述业务流程并具有智能系统生成可以自动执行的工作流程将是有益的。过程自动化的基础是如果是程序。在消费者空间中，IFTTT和Zapier之类的网站允许用户通过使用图形接口定义然后定义程序来创建自动化。如果将程序作为序列学习任务，我们将探索建模的功效。我们发现SEQ2SEQ方法具有很高的潜力（在Zapier食谱上执行强烈），并且可以作为更复杂的程序综合挑战的有前途的方法。
Implementing enterprise process automation often requires significant technical expertise and engineering effort. It would be beneficial for non-technical users to be able to describe a business process in natural language and have an intelligent system generate the workflow that can be automatically executed. A building block of process automations are If-Then programs. In the consumer space, sites like IFTTT and Zapier allow users to create automations by defining If-Then programs using a graphical interface. We explore the efficacy of modeling If-Then programs as a sequence learning task. We find Seq2Seq approaches have high potential (performing strongly on the Zapier recipes) and can serve as a promising approach to more complex program synthesis challenges.
