论文标题
情感不是一个壁炉编码：用灰度标签学习以进行对话中的情感识别
The Emotion is Not One-hot Encoding: Learning with Grayscale Label for Emotion Recognition in Conversation
论文作者
论文摘要
在对话中的情感识别（ERC）中，通过考虑以前的上下文可以预测当前话语的情感，这可以在许多自然语言处理任务中使用。尽管多种情绪可以在给定的句子中共存，但以前的大多数方法都采用分类任务的观点来仅预测给定标签。但是，用自信或多标签标记句子的情绪是昂贵且困难的。在本文中，我们将自动构建一个灰度标签，考虑到情绪之间的相关性并将其用于学习。也就是说，我们不是使用给定标签作为单热编码，而是通过测量不同情绪的分数来构建灰度标签。我们介绍了几种构建灰度标签的方法，并确认每种方法都可以提高情绪识别性能。我们的方法简单，有效且普遍适用于以前的系统。实验显示基线的性能有显着改善。
In emotion recognition in conversation (ERC), the emotion of the current utterance is predicted by considering the previous context, which can be utilized in many natural language processing tasks. Although multiple emotions can coexist in a given sentence, most previous approaches take the perspective of a classification task to predict only a given label. However, it is expensive and difficult to label the emotion of a sentence with confidence or multi-label. In this paper, we automatically construct a grayscale label considering the correlation between emotions and use it for learning. That is, instead of using a given label as a one-hot encoding, we construct a grayscale label by measuring scores for different emotions. We introduce several methods for constructing grayscale labels and confirm that each method improves the emotion recognition performance. Our method is simple, effective, and universally applicable to previous systems. The experiments show a significant improvement in the performance of baselines.
