  Publicly traded companies are required to submit periodic reports with
eXtensive Business Reporting Language (XBRL) word-level tags. Manually tagging
the reports is tedious and costly. We, therefore, introduce XBRL tagging as a
new entity extraction task for the financial domain and release FiNER-139, a
dataset of 1.1M sentences with gold XBRL tags. Unlike typical entity extraction
datasets, FiNER-139 uses a much larger label set of 139 entity types. Most
annotated tokens are numeric, with the correct tag per token depending mostly
on context, rather than the token itself. We show that subword fragmentation of
numeric expressions harms BERT's performance, allowing word-level BILSTMs to
perform better. To improve BERT's performance, we propose two simple and
effective solutions that replace numeric expressions with pseudo-tokens
reflecting original token shapes and numeric magnitudes. We also experiment
with FIN-BERT, an existing BERT model for the financial domain, and release our
own BERT (SEC-BERT), pre-trained on financial filings, which performs best.
Through data and error analysis, we finally identify possible limitations to
inspire future work on XBRL tagging.
