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.

翻译：在线对话有时会因为系统性的文化差异、意外误解或纯粹的恶意而变得更糟。 自动预测公共在线对话中的脱轨为采取早期行动提供了一个机会。 先前在这一空间的工作是有限的,我们以几种方式加以扩展。 我们对这一任务应用了预先培训的语言编码器,这比早先的做法要好。 我们进一步试验将任务的培训模式从静态转向动态模式,以增加预测前景。 这种方法显示的结果好坏参半:在高质量的数据设置中,以F1小幅下降的代价实现较长的平均预测前景;然而,在低质量的数据设置中,动态培训会传播噪音,对业绩极为有害。