Evaluating Sequence-to-Sequence Learning Models for If-Then Program Synthesis
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.
Cite
@article{arxiv.2002.03485,
title = {Evaluating Sequence-to-Sequence Learning Models for If-Then Program Synthesis},
author = {Dhairya Dalal and Byron V. Galbraith},
journal= {arXiv preprint arXiv:2002.03485},
year = {2020}
}
Comments
AAAI IPA workshop submission