  Despite recent advancements in tabular language model research, real-world
applications are still challenging. In industry, there is an abundance of
tables found in spreadsheets, but acquisition of substantial amounts of labels
is expensive, since only experts can annotate the often highly technical and
domain-specific tables. Active learning could potentially reduce labeling
costs, however, so far there are no works related to active learning in
conjunction with tabular language models. In this paper we investigate
different acquisition functions in a real-world industrial tabular language
model use case for sub-cell named entity recognition. Our results show that
cell-level acquisition functions with built-in diversity can significantly
reduce the labeling effort, while enforced table diversity is detrimental. We
further see open fundamental questions concerning computational efficiency and
the perspective of human annotators.
