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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