Evaluating Sequence-to-Sequence Learning Models for If-Then Program Synthesis
This could help non-technical users automate business processes more easily, but it is incremental as it applies existing methods to a new domain.
The paper tackled the problem of generating If-Then programs for enterprise process automation from natural language descriptions, finding that Seq2Seq models perform strongly on Zapier recipes and show promise for complex program synthesis.
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.