Widget Captioning: Generating Natural Language Description for Mobile User Interface Elements

Yang Li, Gang Li, Luheng He, Jingjie Zheng, Hong Li, Zhiwei Guan


Abstract
Natural language descriptions of user interface (UI) elements such as alternative text are crucial for accessibility and language-based interaction in general. Yet, these descriptions are constantly missing in mobile UIs. We propose widget captioning, a novel task for automatically generating language descriptions for UI elements from multimodal input including both the image and the structural representations of user interfaces. We collected a large-scale dataset for widget captioning with crowdsourcing. Our dataset contains 162,860 language phrases created by human workers for annotating 61,285 UI elements across 21,750 unique UI screens. We thoroughly analyze the dataset, and train and evaluate a set of deep model configurations to investigate how each feature modality as well as the choice of learning strategies impact the quality of predicted captions. The task formulation and the dataset as well as our benchmark models contribute a solid basis for this novel multimodal captioning task that connects language and user interfaces.
Anthology ID:
2020.emnlp-main.443
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5495–5510
Language:
URL:
https://www.aclweb.org/anthology/2020.emnlp-main.443
DOI:
10.18653/v1/2020.emnlp-main.443
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.emnlp-main.443.pdf