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.

翻译：多作者写作风格检测任务旨在识别给定文本文档中写作风格发生变化的任何位置。我们将该任务定义为自然语言推理问题，即对两个连续段落进行配对。我们的方法侧重于段落间的过渡，同时针对任务对输入词元进行截断。作为骨干模型，我们采用了不同的基于Transformer的编码器，并在训练过程中引入预热阶段。我们提交的模型版本在实验中优于基线模型及其他提出的模型变体。针对简单和中等设置，我们提交了基于DeBERTa的过渡式自然语言推理模型，并采用预热训练；针对困难设置，则提交了去除过渡机制的相同模型。