  The main goal of this research is to produce a useful software for United
Nations (UN), that could help to speed up the process of qualifying the UN
documents following the Sustainable Development Goals (SDGs) in order to
monitor the progresses at the world level to fight poverty, discrimination,
climate changes. In fact human labeling of UN documents would be a daunting
task given the size of the impacted corpus. Thus, automatic labeling must be
adopted at least as a first step of a multi-phase process to reduce the overall
effort of cataloguing and classifying. Deep Learning (DL) is nowadays one of
the most powerful tools for state-of-the-art (SOTA) AI for this task, but very
often it comes with the cost of an expensive and error-prone preparation of a
training-set. In the case of multi-label text classification of domain-specific
text it seems that we cannot effectively adopt DL without a big-enough
domain-specific training-set. In this paper, we show that this is not always
true. In fact we propose a novel method that is able, through statistics like
TF-IDF, to exploit pre-trained SOTA DL models (such as the Universal Sentence
Encoder) without any need for traditional transfer learning or any other
expensive training procedure. We show the effectiveness of our method in a
legal context, by classifying UN Resolutions according to their most related
SDGs.
