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ScreenQA: Large-Scale Question-Answer Pairs over Mobile App Screenshots

North American Chapter of the Association for Computational Linguistics (NAACL), 2022
16 September 2022
Yu-Chung Hsiao
Fedir Zubach
Maria Wang
Jindong Chen
    RALM
ArXiv (abs)PDFHTML
Abstract

We present a new task and dataset, ScreenQA, for screen content understanding via question answering. The existing screen datasets are focused either on structure and component-level understanding, or on a much higher-level composite task such as navigation and task completion. We attempt to bridge the gap between these two by annotating 86K question-answer pairs over the RICO dataset in hope to benchmark the screen reading comprehension capacity.

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