  Specialized transformer-based models for encoding tabular data have gained
interest in academia. Although tabular data is omnipresent in industry,
applications of table transformers are still missing. In this paper, we study
how these models can be applied to an industrial Named Entity Recognition (NER)
problem where the entities are mentioned in tabular-structured spreadsheets.
The highly technical nature of spreadsheets as well as the lack of labeled data
present major challenges for fine-tuning transformer-based models. Therefore,
we develop a dedicated table data augmentation strategy based on available
domain-specific knowledge graphs. We show that this boosts performance in our
low-resource scenario considerably. Further, we investigate the benefits of
tabular structure as inductive bias compared to tables as linearized sequences.
Our experiments confirm that a table transformer outperforms other baselines
and that its tabular inductive bias is vital for convergence of
transformer-based models.
