  Neural Table-to-Text models tend to hallucinate, producing texts that contain
factual errors. We investigate whether such errors in the output can be traced
back to problems with the input. We manually annotated 1,837 texts generated by
multiple models in the politics domain of the ToTTo dataset. We identify the
input problems that are responsible for many output errors and show that fixing
these inputs reduces factual errors by between 52% and 76% (depending on the
model). In addition, we observe that models struggle in processing tabular
inputs that are structured in a non-standard way, particularly when the input
lacks distinct row and column values or when the column headers are not
correctly mapped to corresponding values.
