论文标题
宇宙：对话中情感识别的常识知识
COSMIC: COmmonSense knowledge for eMotion Identification in Conversations
论文作者
论文摘要
在本文中，我们使用常识知识解决了对话中话语水平情感识别的任务。我们提出了宇宙，这是一个新的框架，结合了常识性的不同要素，例如精神状态，事件和因果关系，并在他们的基础上学习参加对话的对话者之间的互动。当前的最新方法通常在上下文传播，情感转移检测以及相关情绪类别之间遇到困难。通过学习不同的常识性表示，宇宙解决了这些挑战，并在四个不同的基准对话数据集上实现了新的最新结果，以识别情感。我们的代码可在https://github.com/declare-lab/conv-emotion上找到。
In this paper, we address the task of utterance level emotion recognition in conversations using commonsense knowledge. We propose COSMIC, a new framework that incorporates different elements of commonsense such as mental states, events, and causal relations, and build upon them to learn interactions between interlocutors participating in a conversation. Current state-of-the-art methods often encounter difficulties in context propagation, emotion shift detection, and differentiating between related emotion classes. By learning distinct commonsense representations, COSMIC addresses these challenges and achieves new state-of-the-art results for emotion recognition on four different benchmark conversational datasets. Our code is available at https://github.com/declare-lab/conv-emotion.
