Online conversations can sometimes take a turn for the worse, either due to
systematic cultural differences, accidental misunderstandings, or mere malice.
Automatically forecasting derailment in public online conversations provides an
opportunity to take early action to moderate it. Previous work in this space is
limited, and we extend it in several ways. We apply a pretrained language
encoder to the task, which outperforms earlier approaches. We further
experiment with shifting the training paradigm for the task from a static to a
dynamic one to increase the forecast horizon. This approach shows mixed
results: in a high-quality data setting, a longer average forecast horizon can
be achieved at the cost of a small drop in F1; in a low-quality data setting,
however, dynamic training propagates the noise and is highly detrimental to
performance.

自动预测公开在线对话中的偏离度可提供早期干预机会。我们将预训练语言编码器应用于此任务，并尝试将训练范式从静态转换为动态以增加预测范围。该方法在高质量数据环境下表现出更长平均预测范围，但在低质量数据环境下动态训练会传播噪音并对性能产生负面影响。