论文标题
演讲者指导的编码器框架在对话中识别情感
Speaker-Guided Encoder-Decoder Framework for Emotion Recognition in Conversation
论文作者
论文摘要
对话（ERC）任务中的情感识别旨在预测对话中发音的情感标签。由于说话者之间的依赖性是复杂且动态的，这些依赖性由言论和扬声器间依赖性组成，因此说话者特定信息的建模是ERC中的至关重要的作用。尽管现有的研究人员提出了各种说话者交互模型的方法，但他们不能共同探索动态的言论和言论者的依赖性，从而导致对上下文的理解不足并进一步阻碍情绪预测。为此，我们设计了一种新颖的扬声器建模方案，该方案以动态方式共同探索言论和言论者间依赖性。此外，我们为ERC提出了一个演讲者指导的编码编码器（SGED）框架，该框架充分利用了扬声器信息以解码情感。我们使用不同的现有方法作为我们框架的对话上下文编码器，显示了提出的框架的高扩展性和灵活性。实验结果证明了SGED的优势和有效性。
The emotion recognition in conversation (ERC) task aims to predict the emotion label of an utterance in a conversation. Since the dependencies between speakers are complex and dynamic, which consist of intra- and inter-speaker dependencies, the modeling of speaker-specific information is a vital role in ERC. Although existing researchers have proposed various methods of speaker interaction modeling, they cannot explore dynamic intra- and inter-speaker dependencies jointly, leading to the insufficient comprehension of context and further hindering emotion prediction. To this end, we design a novel speaker modeling scheme that explores intra- and inter-speaker dependencies jointly in a dynamic manner. Besides, we propose a Speaker-Guided Encoder-Decoder (SGED) framework for ERC, which fully exploits speaker information for the decoding of emotion. We use different existing methods as the conversational context encoder of our framework, showing the high scalability and flexibility of the proposed framework. Experimental results demonstrate the superiority and effectiveness of SGED.
