In this article, we propose a novel reinforcement learning network that keeps track of the gradual emotional changes from every utterance throughout the conversation and uses this information for each utterance’s emotion detection. Concretely, we first establish an agent and, then, utilize sliding windows to extract the accumulated emotional information before the current utterance. We define the concatenation of accumulated emotional information and the contextual information as the state of the reinforcement learning framework. The action of the established agent is formulated as the emotional label of the current utterance. On this basis, we formulate the progressive emotional interaction process throughout the conversation as a sequential decision problem and solve it with the reinforcement learning framework. Detailed evaluations on the published multimodal MELD dataset demonstrate the effectiveness of our approach.

中文翻译：

---

基于强化学习框架的对话情感检测

在本文中，我们提出了一种新颖的强化学习网络，它跟踪整个对话中每个话语的逐渐情绪变化，并将这些信息用于每个话语的情绪检测。具体来说，我们首先建立一个代理，然后利用滑动窗口来提取当前话语之前积累的情感信息。我们将累积的情感信息和上下文信息的串联定义为强化学习框架的状态。已建立代理的行为被表述为当前话语的情感标签。在此基础上，我们将贯穿整个对话的渐进式情感交互过程制定为一个顺序决策问题，并用强化学习框架解决。