German FinBERT For QuAD (Further Pre-trained Version, Fine-Tuned for Financial Question Answering)

German FinBERT is a BERT language model focusing on the financial domain within the German language. In my paper, I describe in more detail the steps taken to train the model and show that it outperforms its generic benchmarks for finance specific downstream tasks.

This model is the further-pretrained version of German FinBERT, after fine-tuning on the German Ad-Hoc QuAD dataset.

Overview

Author Moritz Scherrmann Paper: here
Architecture: BERT base Language: German
Specialization: Financial question answering Base model: German_FinBert_FP

Fine-tuning

I fine-tune the model using the 1cycle policy of Smith and Topin (2019). I use the Adam optimization method of Kingma and Ba (2014) with standard parameters.I run a grid search on the evaluation set to find the best hyper-parameter setup. I test different values for learning rate, batch size and number of epochs, following the suggestions of Chalkidis et al. (2020). I repeat the fine-tuning for each setup five times with different seeds, to avoid getting good results by chance. After finding the best model w.r.t the evaluation set, I report the mean result across seeds for that model on the test set.

Results

Ad-Hoc QuAD (Question Answering):

Exact Match (EM): 52.50%
F1 Score: 74.61%

Authors

Moritz Scherrmann: scherrmann [at] lmu.de

For additional details regarding the performance on fine-tune datasets and benchmark results, please refer to the full documentation provided in the study.