  The wide-spread use of social networks has given rise to subjective,
misleading, and even false information on the Internet. Thus, subjectivity
detection can play an important role in ensuring the objectiveness and the
quality of a piece of information. This paper presents the solution built by
the Gpachov team for the CLEF-2023 CheckThat! lab Task~2 on subjectivity
detection. Three different research directions are explored. The first one is
based on fine-tuning a sentence embeddings encoder model and dimensionality
reduction. The second one explores a sample-efficient few-shot learning model.
The third one evaluates fine-tuning a multilingual transformer on an altered
dataset, using data from multiple languages. Finally, the three approaches are
combined in a simple majority voting ensemble, resulting in 0.77 macro F1 on
the test set and achieving 2nd place on the English subtask.
