This repo contains the code for Evaluating Sequence-to-Sequence Learning Models for If-Then Program Synthesis.

Abstract: Implementing enterprise process automation often requiressignificant 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 IFTTTand Zapier allow users to create automations by defining If-Then programs using a graphical interface. We explore the ef-ficacy of modeling If-Then programs as a sequence learningtask. We find Seq2Seq approaches have high potential (performing strongly on the Zapier recipes) and can serve as apromising approach to more complex program synthesis challenges.