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Inferring Pragmatics from Dialogue Contexts in Simulated Virtual Agent Games

机译：从模拟虚拟代理游戏的对话上下文中推断语用学

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Virtual agents in video games may conduct two types of interactions: physical and dialogical. While the former is recognized as gazes and gestures, which received significant attention, the latter is often simplified in simulated virtual agent games. However, dialogical interactions affect the mental states of individual agents, and the relations between them, therefore playing a more important role than physical interactions in games. An implemented dynamic Bayesian Network (DBN) based on speech acts is proposed to model the dialogical effects as dialogue contexts in different aspects, such as emotion states, social relations, and social roles. We adopt a scene in the famous movie Doubt that has 53 dialogue sentences as the test corpus and implement 21 types of speech acts in the experiments. The results indicate that, with our DBN model, agents have the ability of context awareness to infer indirect speech acts from given direct speech acts, and that this ability may assist agents to plan dialogues based on speech acts in future work.
机译：电子游戏中的虚拟代理可以进行两种类型的交互：物理交互和对话交互。前者被认为是凝视和手势，受到了极大的关注，而后者通常在模拟的虚拟代理游戏中得以简化。但是，对话互动会影响个体特工的心理状态及其之间的关系，因此比游戏中的物理互动更重要。提出了一种基于语音行为的动态贝叶斯网络（DBN），以将对话效果建模为不同方面的对话上下文，例如情感状态，社会关系和社会角色。我们采用了著名电影《怀疑》中的一个场景，该场景具有53个对话句子作为测试语料，并在实验中实现了21种语音行为。结果表明，使用我们的DBN模型，座席具有上下文感知能力，可以从给定的直接言语行为中推断出间接言语行为，并且这种能力可以在未来的工作中帮助座席计划基于言语行为的对话。

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