论文标题
通过辅助扬声器识别的多任务学习以进行对话情感识别
Multi-Task Learning with Auxiliary Speaker Identification for Conversational Emotion Recognition
论文作者
论文摘要
对话情感识别（CER）吸引了对自然语言处理（NLP）社区的兴趣越来越多。与香草情绪的识别不同，有效的说话者的话语表示是CER的主要挑战。在本文中，我们利用说话者身份（SI）作为一项辅助任务，以增强对话中的话语表示。通过这种方法，我们可以从其他SI语料库中学习更好的说话者感知的上下文表示。两个基准数据集的实验表明，所提出的体系结构对CER非常有效，在两个数据集上获得了新的最新结果。
Conversational emotion recognition (CER) has attracted increasing interests in the natural language processing (NLP) community. Different from the vanilla emotion recognition, effective speaker-sensitive utterance representation is one major challenge for CER. In this paper, we exploit speaker identification (SI) as an auxiliary task to enhance the utterance representation in conversations. By this method, we can learn better speaker-aware contextual representations from the additional SI corpus. Experiments on two benchmark datasets demonstrate that the proposed architecture is highly effective for CER, obtaining new state-of-the-art results on two datasets.
