  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.
