  Classifying public tenders is a useful task for both companies that are
invited to participate and for inspecting fraudulent activities. To facilitate
the task for both participants and public administrations, the European Union
presented a common taxonomy (Common Procurement Vocabulary, CPV) which is
mandatory for tenders of certain importance; however, the contracts in which a
CPV label is mandatory are the minority compared to all the Public
Administrations activities. Classifying over a real-world taxonomy introduces
some difficulties that can not be ignored. First of all, some fine-grained
classes have an insufficient (if any) number of observations in the training
set, while other classes are far more frequent (even thousands of times) than
the average. To overcome those difficulties, we present a zero-shot approach,
based on a pre-trained language model that relies only on label description and
respects the label taxonomy. To train our proposed model, we used industrial
data, which comes from contrattipubblici.org, a service by SpazioDati s.r.l.
that collects public contracts stipulated in Italy in the last 25 years.
Results show that the proposed model achieves better performance in classifying
low-frequent classes compared to three different baselines, and is also able to
predict never-seen classes.
