Speakers
Description
This research presents a comparative analysis of
Natural Language Understanding (NLU) and Natural Language
Generation (NLG) models for the task of fake news detection. A
concise literature review was conducted to understand the state-
of-the-art techniques in the field. The study focused on comparing
the performance of two language models, BERT (Bidirectional
Encoder Representations from Transformers) and GPT-2 (Gen-
erative Pre-trained Transformer 2). The evaluation involved the
application of various metrics to assess the effectiveness of
each model. Additionally, the research utilized data visualization
techniques to gain insights into the models’ decision-making
processes. The findings of this comparative study contribute to
our understanding of the strengths and limitations of NLU and
NLG approaches in the context of fake news detection, providing
valuable insights for the development of robust and reliable
misinformation detection systems.
