ARC-NLP at PAN 2023: Transition-Focused Natural Language Inference for
  Writing Style Detection
Izzet Emre Kucukkaya Umitcan Sahin Cagri Toraman
The task of multi-author writing style detection aims at finding any
positions of writing style change in a given text document. We formulate the
task as a natural language inference problem where two consecutive paragraphs
are paired. Our approach focuses on transitions between paragraphs while
truncating input tokens for the task. As backbone models, we employ different
Transformer-based encoders with warmup phase during training. We submit the
model version that outperforms baselines and other proposed model versions in
our experiments. For the easy and medium setups, we submit transition-focused
natural language inference based on DeBERTa with warmup training, and the same
model without transition for the hard setup.