ARC-NLP at PAN 2023: Transition-Focused Natural Language Inference for Writing Style Detection


Kucukkaya I. E., Sahin U., Toraman Ç.

24th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF-WN 2023, Thessaloniki, Yunanistan, 18 - 21 Eylül 2023, cilt.3497, ss.2659-2668 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 3497
  • Basıldığı Şehir: Thessaloniki
  • Basıldığı Ülke: Yunanistan
  • Sayfa Sayıları: ss.2659-2668
  • Anahtar Kelimeler: Multi-author, natural language inference, transition, writing style detection
  • Orta Doğu Teknik Üniversitesi Adresli: Hayır

Özet

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.