Emotion recognition in conversation (ERC) is a widely used technology in both affective dialogue bots and dialogue recommendation scenarios, where motivating a system to correctly recognize human emotions is crucial. Uncovering as much contextual information as possible with a limited amount of dialogue information is essential for eventually identifying the correct emotion of each sentence. The integration of contextual information using the existing approaches often results in inadequate access to information or information redundancy. Deeply integrating the different knowledge behind utterances is also difficult. Therefore, to address these problems, we propose a dynamic complementary graph convolutional network (DCGCN) for conversational emotion recognition. Our approach uses commonsense knowledge to complement the contextual information contained in utterances and enrich the extracted conversation information. We creatively propose the concept of utterance density to prevent redundancy and the loss of utterance information in context-dependent contextual information modeling cases. An utterance dependency structure is dynamically determined by the utterance density, and the contextual information is fully integrated into each sentence representation. We evaluate our proposed model in extensive experiments conducted on four public benchmark datasets that are commonly used for ERC. The results demonstrate the effectiveness of the DCGCN, which achieves competitive results in terms of well-known evaluation metrics.

中文翻译：

---

基于动态互补图卷积网络的对话情绪识别


对话中的情绪识别（ERC）是情感对话机器人和对话推荐场景中广泛使用的技术，其中激励系统正确识别人类情绪至关重要。用有限的对话信息揭示尽可能多的上下文信息对于最终识别每个句子的正确情感至关重要。使用现有方法集成上下文信息通常会导致信息访问不足或信息冗余。深度整合话语背后的不同知识也很困难。因此，为了解决这些问题，我们提出了一种用于会话情感识别的动态互补图卷积网络（DCGCN）。我们的方法使用常识知识来补充话语中包含的上下文信息并丰富提取的对话信息。我们创造性地提出了话语密度的概念，以防止上下文相关的上下文信息建模案例中的冗余和话语信息丢失。话语依赖结构由话语密度动态确定，并且上下文信息完全集成到每个句子表示中。我们在 ERC 常用的四个公共基准数据集上进行的广泛实验中评估了我们提出的模型。结果证明了 DCGCN 的有效性，在众所周知的评估指标方面取得了有竞争力的结果。