Disagreements are pervasive in human communication. In this paper we
investigate what makes disagreement constructive. To this end, we construct
WikiDisputes, a corpus of 7 425 Wikipedia Talk page conversations that contain
content disputes, and define the task of predicting whether disagreements will
be escalated to mediation by a moderator. We evaluate feature-based models with
linguistic markers from previous work, and demonstrate that their performance
is improved by using features that capture changes in linguistic markers
throughout the conversations, as opposed to averaged values. We develop a
variety of neural models and show that taking into account the structure of the
conversation improves predictive accuracy, exceeding that of feature-based
models. We assess our best neural model in terms of both predictive accuracy
and uncertainty by evaluating its behaviour when it is only exposed to the
beginning of the conversation, finding that model accuracy improves and
uncertainty reduces as models are exposed to more information.

研究建立在 WikiDisputes 的语料库上，使用特征和神经网络模型，预测带有争议的维基百科讨论页面是否会被调解员升级为调解。结果表明，利用能够捕捉对话过程中语言标记变化的特征，和考虑对话结构的神经网络模型能更准确地预测结果。