Accurate generation of trigger-action programs with domain-adapted sequence-to-sequence learning

• Main Authors: IMAM NUR BANI YUSUF, JIANG, Lingxiao, LO, David
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Artificial Intelligence and Robotics
• Online Access: https://ink.library.smu.edu.sg/sis_research/7723; https://ink.library.smu.edu.sg/context/sis_research/article/8726/viewcontent/ICPC22RecipeGen.pdf
• Institution: Singapore Management University

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 overall improvement against the prior best results ranges from 9.5%-26.5%. Our results also show that adopting a pre-trained autoencoding model boosts the MRR@3 further by 2.8%-10.8%. Further, in the field-level generation setting, RecipeGen achieves 0.591 and 0.575 in terms of MRR@3 and BLEU scores respectively.