Conversations among online users sometimes derail, i.e., break down into
personal attacks. Such derailment has a negative impact on the healthy growth
of cyberspace communities. The ability to predict whether ongoing conversations
are likely to derail could provide valuable real-time insight to interlocutors
and moderators. Prior approaches predict conversation derailment
retrospectively without the ability to forestall the derailment proactively.
Some works attempt to make dynamic prediction as the conversation develops, but
fail to incorporate multisource information, such as conversation structure and
distance to derailment.
We propose a hierarchical transformer-based framework that combines
utterance-level and conversation-level information to capture fine-grained
contextual semantics. We propose a domain-adaptive pretraining objective to
integrate conversational structure information and a multitask learning scheme
to leverage the distance from each utterance to derailment. An evaluation of
our framework on two conversation derailment datasets yields improvement over
F1 score for the prediction of derailment. These results demonstrate the
effectiveness of incorporating multisource information.

提出一种基于分层 Transformer 的框架，结合话语级别和对话级别信息捕捉精细的上下文语义，同时考虑话语结构信息和距离变形的多任务学习方案，以实现对网络对话中马车出轨的预测。