基于多方注意力建模的对话情感分类

Dialog Sentiment Analysis with Multi-party Attention

对话情感分析旨在对一段对话中的每个句子进行情感分类,既要考虑到说话者个人的情感惯性,也要考虑到说话者之间的情感传递,对于构建具有移情功能的对话系统等具有重要作用。在目前已有的工作中,多数是基于循环神经网络构建记忆网络对说话者建模,该文从基于Transformer的对话建模的角度出发,为了多方注意力机制建模不同说话者之间的交互,更好地模拟对话场景。实验表明,该文提出的Dialogue Transformer相较于其他前沿模型,其实现简洁,运行速率更快,且加权F 1值也有较大提高。

Dialog sentiment analysis aims to classify the sentiment of each sentence in a dialogue,considering both the speaker’s personal emotion and the emotion transmission between speakers.To model this with Transformer,this paper proposes a multi-party attention mechanism to better model the interaction between different speakers and simulate dialogue scenes.Experiments show that,compared with other SOTA models,Dialogue Transformer has simpler implementation,faster running speed,and an significantly increased Weighted-F 1 value.