使用图卷积网络的知识感知对话脱轨预测
Knowledge-Aware Conversation Derailment Forecasting Using Graph Convolutional Networks
AI总结：
针对在线对话脱轨预测中上下文传播与情绪转变难以建模的问题，本文融合常识知识库信息和多源话语胶囊，提出基于图卷积网络与 transformer 的知识感知预测框架，并在 CGA、CMV 上超越现有最优模型。
摘要：
在线对话特别容易发生脱轨，其表现形式可能是包括无礼评论和辱骂在内的有毒沟通模式。预测对话脱轨可提前发现脱轨迹象，从而实现对对话的主动审核。当前最先进的对话脱轨预测方法按顺序编码对话，并使用图神经网络对对话用户动态进行建模。然而，现有图模型无法捕捉复杂的对话特征，例如上下文传播和情绪转变。使用常识知识可使模型捕捉此类特征，从而提升性能。遵循这一思路，我们在此从包含对话上下文信息的知识库中提取常识陈述，以丰富图神经网络分类架构。我们将话语上的多源信息融合到胶囊中，供基于 transformer 的预测器用于预测对话脱轨。我们的模型能够捕捉对话动态和上下文传播，并在 CGA 和 CMV 基准数据集上优于最先进的模型。
英文摘要：
Online conversations are particularly susceptible to derailment, which can manifest itself in the form of toxic communication patterns including disrespectful comments and abuse. Forecasting conversation derailment predicts signs of derailment in advance enabling proactive moderation of conversations. State-of-the-art approaches to conversation derailment forecasting sequentially encode conversations and use graph neural networks to model dialogue user dynamics. However, existing graph models are not able to capture complex conversational characteristics such as context propagation and emotional shifts. The use of common sense knowledge enables a model to capture such characteristics, thus improving performance. Following this approach, here we derive commonsense statements from a knowledge base of dialogue contextual information to enrich a graph neural network classification architecture. We fuse the multi-source information on utterance into capsules, which are used by a transformer-based forecaster to predict conversation derailment. Our model captures conversation dynamics and context propagation, outperforming the state-of-the-art models on the CGA and CMV benchmark datasets
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
