论文标题
情感识别和对话中的统一框架
A Unified Framework for Emotion Identification and Generation in Dialogues
论文作者
论文摘要
社交聊天机器人已经获得了巨大的知名度，他们的吸引力不仅在于他们对用户的不同要求的响应的能力，而且还具有与用户建立情感联系的能力。为了进一步发展和促进社交聊天机器人，我们需要专注于增加用户互动，并考虑到对话剂中的智力和情感商。在本文中，我们提出了一个多任务框架，该框架共同识别给定对话的情绪，并根据确定的情绪产生响应。我们采用基于BERT的网络来创建善解人意的系统，并使用混合的目标函数，该目标功能可以通过分类和生成损失来训练端到端网络。实验结果表明，我们提出的框架优于当前最新模型
Social chatbots have gained immense popularity, and their appeal lies not just in their capacity to respond to the diverse requests from users, but also in the ability to develop an emotional connection with users. To further develop and promote social chatbots, we need to concentrate on increasing user interaction and take into account both the intellectual and emotional quotient in the conversational agents. In this paper, we propose a multi-task framework that jointly identifies the emotion of a given dialogue and generates response in accordance to the identified emotion. We employ a BERT based network for creating an empathetic system and use a mixed objective function that trains the end-to-end network with both the classification and generation loss. Experimental results show that our proposed framework outperforms current state-of-the-art models
