Few-Shot Learning for Issue Report Classification

We describe our participation in the tool competition in the scope of the 2nd International Workshop on Natural Language-based Software Engineering. We propose a supervised approach relying on SETFIT, a framework for few-shot learning and sentence-BERT (SBERT), a variant of BERT for effective sentence embedding. We experimented with different settings, achieving the best performance by training and testing the SETFIT-based model on a subset of data with manually verified labels (Fl-micro
$=.8321$
). For the sake of the challenge, we evaluate the SETFIT model on the challenge test set, achieving Fl-micro
$=.7767$
.