BUSTER: a "BUSiness Transaction Entity Recognition" dataset
Andrea Zugarini Andrew Zamai Marco Ernandes Leonardo Rigutini
Albeit Natural Language Processing has seen major breakthroughs in the last
few years, transferring such advances into real-world business cases can be
challenging. One of the reasons resides in the displacement between popular
benchmarks and actual data. Lack of supervision, unbalanced classes, noisy data
and long documents often affect real problems in vertical domains such as
finance, law and health. To support industry-oriented research, we present
BUSTER, a BUSiness Transaction Entity Recognition dataset. The dataset consists
of 3779 manually annotated documents on financial transactions. We establish
several baselines exploiting both general-purpose and domain-specific language
models. The best performing model is also used to automatically annotate 6196
documents, which we release as an additional silver corpus to BUSTER.