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

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 trigg...

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Main Authors: IMAM NUR BANI YUSUF, JIANG, Lingxiao, LO, David
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Language:English
Published: Institutional Knowledge at Singapore Management University 2022
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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
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spelling sg-smu-ink.sis_research-87262023-01-10T02:52:03Z Accurate generation of trigger-action programs with domain-adapted sequence-to-sequence learning IMAM NUR BANI YUSUF, JIANG, Lingxiao LO, David 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. 2022-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/7723 info:doi/10.1145/3524610.3527922 https://ink.library.smu.edu.sg/context/sis_research/article/8726/viewcontent/ICPC22RecipeGen.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Artificial Intelligence and Robotics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Artificial Intelligence and Robotics
spellingShingle Artificial Intelligence and Robotics
IMAM NUR BANI YUSUF,
JIANG, Lingxiao
LO, David
Accurate generation of trigger-action programs with domain-adapted sequence-to-sequence learning
description 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.
format text
author IMAM NUR BANI YUSUF,
JIANG, Lingxiao
LO, David
author_facet IMAM NUR BANI YUSUF,
JIANG, Lingxiao
LO, David
author_sort IMAM NUR BANI YUSUF,
title Accurate generation of trigger-action programs with domain-adapted sequence-to-sequence learning
title_short Accurate generation of trigger-action programs with domain-adapted sequence-to-sequence learning
title_full Accurate generation of trigger-action programs with domain-adapted sequence-to-sequence learning
title_fullStr Accurate generation of trigger-action programs with domain-adapted sequence-to-sequence learning
title_full_unstemmed Accurate generation of trigger-action programs with domain-adapted sequence-to-sequence learning
title_sort accurate generation of trigger-action programs with domain-adapted sequence-to-sequence learning
publisher Institutional Knowledge at Singapore Management University
publishDate 2022
url https://ink.library.smu.edu.sg/sis_research/7723
https://ink.library.smu.edu.sg/context/sis_research/article/8726/viewcontent/ICPC22RecipeGen.pdf
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