论文标题
在对话中利用无监督的数据以识别情绪
Exploiting Unsupervised Data for Emotion Recognition in Conversations
论文作者
论文摘要
对话中的情感识别（ERC）旨在预测对话中说话者的情绪状态，这实质上是文本分类任务。与句子级文本分类问题不同，ERC任务的可用监督数据有限，这有可能阻止模型发挥最大效果。在本文中，我们提出了一种新颖的方法来利用无监督的对话数据，这更容易访问。具体来说，我们提出了对话完成（CONSCOM）任务，该任务试图从候选人答案中选择正确的答案，以在对话中填写蒙面的话语。然后，我们在Convcom任务上预先培训基本的依赖性编码器（预编码）。最后，我们在ERC数据集上微调了预编码。实验结果表明，对无监督数据的预训练可以显着改善ERC数据集的性能，尤其是在少数族裔情绪类别上。
Emotion Recognition in Conversations (ERC) aims to predict the emotional state of speakers in conversations, which is essentially a text classification task. Unlike the sentence-level text classification problem, the available supervised data for the ERC task is limited, which potentially prevents the models from playing their maximum effect. In this paper, we propose a novel approach to leverage unsupervised conversation data, which is more accessible. Specifically, we propose the Conversation Completion (ConvCom) task, which attempts to select the correct answer from candidate answers to fill a masked utterance in a conversation. Then, we Pre-train a basic COntext- Dependent Encoder (Pre-CODE) on the ConvCom task. Finally, we fine-tune the Pre-CODE on the datasets of ERC. Experimental results demonstrate that pre-training on unsupervised data achieves significant improvement of performance on the ERC datasets, particularly on the minority emotion classes.
