German FinBERT: A German Pre-trained Language Model
This work addresses the need for better financial text analysis tools in German, offering a domain-specific model that could benefit applications in finance, though it is incremental as it adapts an existing method to new data.
The study tackled the problem of analyzing German financial text by developing German FinBERT, a pre-trained language model tailored for this domain, and demonstrated improved performance on finance-specific tasks like sentiment prediction and question answering compared to generic models.
This study presents German FinBERT, a novel pre-trained German language model tailored for financial textual data. The model is trained through a comprehensive pre-training process, leveraging a substantial corpus comprising financial reports, ad-hoc announcements and news related to German companies. The corpus size is comparable to the data sets commonly used for training standard BERT models. I evaluate the performance of German FinBERT on downstream tasks, specifically sentiment prediction, topic recognition and question answering against generic German language models. My results demonstrate improved performance on finance-specific data, indicating the efficacy of German FinBERT in capturing domain-specific nuances. The presented findings suggest that German FinBERT holds promise as a valuable tool for financial text analysis, potentially benefiting various applications in the financial domain.