META-GUI: Towards Multi-modal Conversational Agents on Mobile GUI

Liangtai Sun, Xingyu Chen, Lu Chen, Tianle Dai, Zichen Zhu, Kai Yu


Abstract
Task-oriented dialogue (TOD) systems have been widely used by mobile phone intelligent assistants to accomplish tasks such as calendar scheduling or hotel reservation. Current TOD systems usually focus on multi-turn text/speech interaction, then they would call back-end APIs designed for TODs to perform the task. However, this API-based architecture greatly limits the information-searching capability of intelligent assistants and may even lead to task failure if TOD-specific APIs are not available or the task is too complicated to be executed by the provided APIs. In this paper, we propose a new TOD architecture: GUI-based task-oriented dialogue system (GUI-TOD). A GUI-TOD system can directly perform GUI operations on real APPs and execute tasks without invoking TOD-specific backend APIs. Furthermore, we release META-GUI, a dataset for training a Multi-modal convErsaTional Agent on mobile GUI. We also propose a multi-model action prediction and response model, which show promising results on META-GUI. The dataset, codes and leaderboard are publicly available.
Anthology ID:
2022.emnlp-main.449
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6699–6712
Language:
URL:
https://aclanthology.org/2022.emnlp-main.449
DOI:
Bibkey:
Cite (ACL):
Liangtai Sun, Xingyu Chen, Lu Chen, Tianle Dai, Zichen Zhu, and Kai Yu. 2022. META-GUI: Towards Multi-modal Conversational Agents on Mobile GUI. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 6699–6712, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
META-GUI: Towards Multi-modal Conversational Agents on Mobile GUI (Sun et al., EMNLP 2022)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.emnlp-main.449.pdf