In a dialog system, dialog act recognition and sentiment classification are two correlative tasks to capture speakers intentions, where dialog act and sentiment can indicate the explicit and the implicit intentions separately. The dialog context information (contextual information) and the mutual interaction information are two key factors that contribute to the two related tasks. Unfortunately, none of the existing approaches consider the two important sources of information simultaneously. In this paper, we propose a Co-Interactive Graph Attention Network (Co-GAT) to jointly perform the two tasks. The core module is a proposed co-interactive graph interaction layer where a cross-utterances connection and a cross-tasks connection are constructed and iteratively updated with each other, achieving to consider the two types of information simultaneously. Experimental results on two public datasets show that our model successfully captures the two sources of information and achieve the state-of-the-art performance. In addition, we find that the contributions from the contextual and mutual interaction information do not fully overlap with contextualized word representations (BERT, Roberta, XLNet).
翻译:在对话系统中,对话行为识别和情绪分类是收集发言者意图的两个相关任务,其中对话行为和情绪可以分别表明明确和隐含的意图。对话背景信息(背景信息)和相互互动信息是有助于两项相关任务的两个关键因素。不幸的是,现有方法中没有一个同时考虑这两个重要的信息来源。在本文件中,我们提议建立一个共同互动图形关注网络(Co-GAT),以共同执行这两项任务。核心模块是一个拟议的共同互动图形互动层,其中相互交错连接和交叉任务连接可以建立并相互迭接更新,同时考虑两种类型的信息。两个公共数据集的实验结果显示,我们的模型成功捕捉了两个信息来源并实现了最新业绩。此外,我们发现,背景和相互互动信息的贡献并不与背景化的词表(BERT、Roberta、XLNet)完全重叠。