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 (\textit{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 \url{contrattipubblici.org}, a service by
\href{this https URL}{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.

使用零样本研究方法，基于预训练的语言模型和标签分类法，对公共招标进行分类，并且在低频类别分类表现上优于三个不同的基线模型，并能够预测未见过的类别。