KPI-BERT: A Joint Named Entity Recognition and Relation Extraction Model for Financial Reports
This addresses the challenge of automating financial report analysis for companies and analysts, but it is incremental as it builds on existing BERT-based methods with specific enhancements.
The paper tackles the problem of extracting and linking key performance indicators from German financial documents using a joint named entity recognition and relation extraction model, achieving substantially higher prediction performance on a new practical dataset and outperforming strong baselines including a state-of-the-art approach.
We present KPI-BERT, a system which employs novel methods of named entity recognition (NER) and relation extraction (RE) to extract and link key performance indicators (KPIs), e.g. "revenue" or "interest expenses", of companies from real-world German financial documents. Specifically, we introduce an end-to-end trainable architecture that is based on Bidirectional Encoder Representations from Transformers (BERT) combining a recurrent neural network (RNN) with conditional label masking to sequentially tag entities before it classifies their relations. Our model also introduces a learnable RNN-based pooling mechanism and incorporates domain expert knowledge by explicitly filtering impossible relations. We achieve a substantially higher prediction performance on a new practical dataset of German financial reports, outperforming several strong baselines including a competing state-of-the-art span-based entity tagging approach.