Evaluating Sequence-to-Sequence Learning Models for If-Then Program Synthesis

• URL: http://arxiv.org/abs/2002.03485v1
• Date: Mon, 10 Feb 2020 00:45:03 GMT
• Title: Evaluating Sequence-to-Sequence Learning Models for If-Then Program Synthesis
• Authors: Dhairya Dalal and Byron V. Galbraith
• Abstract summary: A building block of process automations are If-Then programs. In the consumer space, sites like IFTTT and allow users to create automations by defining If-Then programs using a graphical interface. We find Seq2Seq approaches have high potential (performing strongly on the sequence recipes) and can serve as a promising approach to synthesis more complex program challenges.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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