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

Dhairya Dalal; Byron V. Galbraith

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arXiv · 2002.03485

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

Dhairya Dalal·Byron V. Galbraith

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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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BibTeX RIS

Dhairya Dalal, Byron V. Galbraith. 2020-02-10. Evaluating Sequence-to-Sequence Learning Models for If-Then Program Synthesis. https://arxiv.org/abs/2002.03485

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