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Accurate Generation of Trigger-action Programs with Domain-adapted Sequence-to-sequence Learning by Imam Nur Bani Yusuf; Lingxiao Jiang; David Lo is a scholarly article available to read on EtoBox.

What is Accurate Generation of Trigger-action Programs with Domain-adapted Sequence-to-sequence Learning about?

Trigger-action programming allows end users to write event-driven rules to automate smart devices and internet services. Users can create a trigger-action program (TAP) by specifying triggers and actions from a set of predefined functions along with suitable data fields for the functions. Many trigger-action programming platforms have emerged as the popularity grows, e.g., IFTTT, Microsoft Power Automate, and Samsung SmartThings. Despite their simplicity, composing trigger-action programs (TAPs) can still be challenging for end users due to the domain knowledge needed and enormous search space of many combinations of triggers and actions. We propose RecipeGen, a new deep learning-based approach that leverages Transformer sequence-to-sequence (seq2seq) architecture to generate TAPs on the fine-grained field-level granularity from natural language descriptions. Our approach adapts autoencoding pre-trained models to warm-start the encoder in the seq2seq model to boost the generation performance. We have evaluated RecipeGen on real-world datasets from the IFTTT platform against the prior state-of-the-art approach on the TAP generation task. Our empirical evaluation shows that the overa

Author

Imam Nur Bani Yusuf; Lingxiao Jiang; David Lo

Publisher

ACM

Published

2022

Language

EN