  This paper proposes a question-answering (QA) benchmark for spatial reasoning
on natural language text which contains more realistic spatial phenomena not
covered by prior work and is challenging for state-of-the-art language models
(LM). We propose a distant supervision method to improve on this task.
Specifically, we design grammar and reasoning rules to automatically generate a
spatial description of visual scenes and corresponding QA pairs. Experiments
show that further pretraining LMs on these automatically generated data
significantly improves LMs' capability on spatial understanding, which in turn
helps to better solve two external datasets, bAbI, and boolQ. We hope that this
work can foster investigations into more sophisticated models for spatial
reasoning over text.
