Few-Shot Learning for Issue Report Classification

May 1, 2023ยท
Giuseppe Colavito
,
Filippo Lanubile
,
Nicole Novielli
ยท 0 min read
Abstract
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.
Type
Publication
2023 IEEE/ACM 2nd International Workshop on Natural Language-Based Software Engineering (NLBSE)