  Thanks to the state-of-the-art Large Language Models (LLMs), language
generation has reached outstanding levels. These models are capable of
generating high quality content, thus making it a challenging task to detect
generated text from human-written content. Despite the advantages provided by
Natural Language Generation, the inability to distinguish automatically
generated text can raise ethical concerns in terms of authenticity.
Consequently, it is important to design and develop methodologies to detect
artificial content. In our work, we present some classification models
constructed by ensembling transformer models such as Sci-BERT, DeBERTa and
XLNet, with Convolutional Neural Networks (CNNs). Our experiments demonstrate
that the considered ensemble architectures surpass the performance of the
individual transformer models for classification. Furthermore, the proposed
SciBERT-CNN ensemble model produced an F1-score of 98.36% on the ALTA shared
task 2023 data.
