TAPASGO: Transfer Learning towards a German-Language Tabular Question Answering Model

Abstract

Processing tabular data holds significant importance across various domains and applications. This study investigates the performance and limitations of fine-tuned models for tabular data analysis, specifically focusing on using fine-tuning mechanics on an English model towards a potential German model. The validation of the effectiveness of the transfer learning approach compares the performance of the fine-tuned German model and of the original English model on test data from the German training set. A potential shortcut that translates the German test data into English serves for comparison. Results reveal that the fine-tuned model outperforms the original model significantly, demonstrating the effectiveness of transfer learning even for a limited amount of training data. One also observes that the English model can effectively process translated German tabular data, albeit with a slight accuracy drop compared to fine-tuning. The model evaluation extends to real-world data extracted from the sustainability reports of a financial institution. The fine-tuned model proves superior in extracting knowledge from these training-unrelated tables, indicating its potential applicability in practical scenarios. This paper also releases the first manually annotated dataset for German Table Question Answering and the related annotation tool.