While neural networks demonstrate a remarkable ability to model linguistic content, capturing contextual information related to a speaker's conversational role is an open area of research. In this work, we analyze the effect of speaker role on language use through the game of Mafia, in which participants are assigned either an honest or a deceptive role. In addition to building a framework to collect a dataset of Mafia game records, we demonstrate that there are differences in the language produced by players with different roles. We confirm that classification models are able to rank deceptive players as more suspicious than honest ones based only on their use of language. Furthermore, we show that training models on two auxiliary tasks outperforms a standard BERT-based text classification approach. We also present methods for using our trained models to identify features that distinguish between player roles, which could be used to assist players during the Mafia game.

翻译：虽然神经网络展示了制作语言内容模型的非凡能力,但获取与演讲者谈话角色相关的背景信息是一个开放的研究领域。 在这项工作中,我们分析了通过黑手党游戏演讲者角色对语言使用的影响,在黑手党游戏中,参与者被指派了诚实或欺骗的角色。除了建立一个收集黑手党游戏记录数据集的框架外,我们还表明,具有不同角色的玩家所制作的语言存在差异。我们确认,分类模型能够将欺骗性玩家列为比诚实玩家更可疑的仅以其使用语言为基础的角色。此外,我们展示了两种辅助任务的培训模式超越了基于BERT的标准文本分类方法。我们还介绍了使用我们经过培训的模型确定角色之间区别特征的方法,这些特征可用于在黑手党游戏中协助玩家。