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Natural language descriptions of user interface (UI) elements such asalternative text are crucial for accessibility and language-based interactionin general. Yet, these descriptions are constantly missing in mobile UIs. Wepropose widget captioning, a novel task for automatically generating languagedescriptions for UI elements from multimodal input including both the image andthe structural representations of user interfaces. We collected a large-scaledataset for widget captioning with crowdsourcing. Our dataset contains 162,859language phrases created by human workers for annotating 61,285 UI elementsacross 21,750 unique UI screens. We thoroughly analyze the dataset, and trainand evaluate a set of deep model configurations to investigate how each featuremodality as well as the choice of learning strategies impact the quality ofpredicted captions. The task formulation and the dataset as well as ourbenchmark models contribute a solid basis for this novel multimodal captioningtask that connects language and user interfaces.

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