Building a socially intelligent agent involves many challenges. One of which is to track the agent's mental state transition and teach the agent to make decisions guided by its value like a human. Towards this end, we propose to incorporate mental state simulation and value modeling into dialogue agents. First, we build a hybrid mental state parser that extracts information from both the dialogue and event observations and maintains a graphical representation of the agent's mind; Meanwhile, the transformer-based value model learns human preferences from the human value dataset, ValueNet. Empirical results show that the proposed model attains state-of-the-art performance on the dialogue/action/emotion prediction task in the fantasy text-adventure game dataset, LIGHT. We also show example cases to demonstrate: (i) how the proposed mental state parser can assist the agent's decision by grounding on the context like locations and objects, and (ii) how the value model can help the agent make decisions based on its personal priorities.
翻译:建设社会智能剂涉及许多挑战。 其中之一是跟踪该剂的精神状态转变,并教导该剂根据人的价值做出决策。 为此,我们提议将精神状态模拟和价值模型纳入对话剂。 首先,我们建立一个混合精神状态分析器,从对话和事件观测中提取信息,并保持该剂思想的图形表达; 与此同时,基于变压器的价值模型从人类价值数据集中学习人类的偏好,价值网。 经验性结果显示,拟议的模型在幻想的文本冒险游戏数据集LIight中,在对话/行动/情感预测任务中达到了最先进的性能。我们还展示了一些实例,以展示:(一) 拟议的精神状态分析器如何通过定位地点和物体等背景来帮助该剂的决策,以及(二) 价值模型如何帮助该剂根据其个人优先事项做出决策。