  Models designed for intelligent process automation are required to be capable
of grounding user interface elements. This task of interface element grounding
is centred on linking instructions in natural language to their target
referents. Even though BERT and similar pre-trained language models have
excelled in several NLP tasks, their use has not been widely explored for the
UI grounding domain. This work concentrates on testing and probing the
grounding abilities of three different transformer-based models: BERT, RoBERTa
and LayoutLM. Our primary focus is on these models' spatial reasoning skills,
given their importance in this domain. We observe that LayoutLM has a promising
advantage for applications in this domain, even though it was created for a
different original purpose (representing scanned documents): the learned
spatial features appear to be transferable to the UI grounding setting,
especially as they demonstrate the ability to discriminate between target
directions in natural language instructions.
