一句话：提出 RecipeGen++，用 Transformer seq2seq 从自然语言生成事件驱动的 TAP 规则，支持三种生成模式并在真实数据上超越基线。
触发器动作程序（TAPs）是事件驱动的规则，让用户自动化智能设备与互联网服务：用户从预定义的 channel 与 function 中挑选 trigger 和 action 来组合规则。尽管书写形式简单，但随着支持设备与服务不断增多，trigger 与 action 的组合空间极其庞大，用户难以从中搜索出所需规则。本文在前期工作基础上提出 RecipeGen++，一种基于 Transformer seq2seq 架构的深度学习方法，以自然语言描述为输入自动生成 TAP。系统提供三种模式：Interactive 模式下用户可对 trigger 或 action 组件的预测给出反馈以引导生成，One-Click 模式直接一次性生成全部 TAP 组件，Functionality Discovery 模式则支持在 channel 层面发现可用功能。作者在真实数据集上对所有模式进行了评估，结果显示 RecipeGen++ 在 gold-standard 基准上较基线提升 2.2%–16.2%，在 noisy 基准上提升 5%–29.2%。
Trigger Action Programs (TAPs) are event-driven rules that allow users to automate smart-devices and internet services. Users can write TAPs by specifying triggers and actions from a set of predefined channels and functions. Despite its simplicity, composing TAPs can still be challenging for users due to the enormous search space of available triggers and actions. The growing popularity of TAPs is followed by the increasing number of supported devices and services, resulting in a huge number of possible combinations between triggers and actions. Motivated by such a fact, we improve our prior work and propose RecipeGen++, a deep-learning-based approach that leverages Transformer seq2seq (sequence-to-sequence) architecture to generate TAPs given natural language descriptions. RecipeGen++ can generate TAPs in the Interactive, One-Click, or Functionality Discovery modes. In the Interactive mode, users can provide feedback to guide the prediction of a trigger or action component. In contrast, the One-Click mode allows users to generate all TAP components directly. Additionally, RecipeGen++ also enables users to discover functionalities at the channel level through the Functionality Discovery mode. We have evaluated RecipeGen++ on real-world datasets in all modes. Our results demonstrate that RecipeGen++ can outperform the baseline by 2.2%-16.2% in the gold-standard benchmark and 5%-29.2% in the noisy benchmark.
DOI 原文 ·
@inproceedings{paperbot7119,
title = {RecipeGen++: an automated trigger action programs generator},
author = {Imam Nur Bani Yusuf and Diyanah Binte Abdul Jamal and Lingxiao Jiang and David Lo},
booktitle = {FSE},
year = {2022},
doi = {10.1145/3540250.3558913}
}