Evaluating Sequence-to-Sequence Learning Models for If-Then Program Synthesis
arXiv:2002.03485
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.
arXiv commentAAAI IPA workshop submission
References in corpus (5)
- Neural Machine Translation by Jointly Learning to Align and Translate
- Effective Approaches to Attention-based Neural Machine Translation
- Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
- Latent Attention For If-Then Program Synthesis
- Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement Learning