Named Entity Recognition in Industrial Tables using Tabular Language Models
This addresses NER for industrial tabular data, an incremental improvement with domain-specific application.
The paper tackled Named Entity Recognition in industrial spreadsheets by developing a table data augmentation strategy using domain-specific knowledge graphs, which boosted performance in low-resource scenarios, with experiments showing that a table transformer outperforms baselines and its tabular inductive bias is vital for convergence.
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.