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

Date

2023-01-01

Author

Kucukkaya, Izzet Emre
Sahin, Umitcan
Toraman, Çağrı

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Multi-author, natural language inference, transition, writing style detection

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85175646892&origin=inward
https://hdl.handle.net/11511/109724

Collections

Department of Computer Engineering, Conference / Seminar