  As mobile devices are becoming ubiquitous, regularly interacting with a
variety of user interfaces (UIs) is a common aspect of daily life for many
people. To improve the accessibility of these devices and to enable their usage
in a variety of settings, building models that can assist users and accomplish
tasks through the UI is vitally important. However, there are several
challenges to achieve this. First, UI components of similar appearance can have
different functionalities, making understanding their function more important
than just analyzing their appearance. Second, domain-specific features like
Document Object Model (DOM) in web pages and View Hierarchy (VH) in mobile
applications provide important signals about the semantics of UI elements, but
these features are not in a natural language format. Third, owing to a large
diversity in UIs and absence of standard DOM or VH representations, building a
UI understanding model with high coverage requires large amounts of training
data.
  Inspired by the success of pre-training based approaches in NLP for tackling
a variety of problems in a data-efficient way, we introduce a new pre-trained
UI representation model called ActionBert. Our methodology is designed to
leverage visual, linguistic and domain-specific features in user interaction
traces to pre-train generic feature representations of UIs and their
components. Our key intuition is that user actions, e.g., a sequence of clicks
on different UI components, reveals important information about their
functionality. We evaluate the proposed model on a wide variety of downstream
tasks, ranging from icon classification to UI component retrieval based on its
natural language description. Experiments show that the proposed ActionBert
model outperforms multi-modal baselines across all downstream tasks by up to
15.5%.
