论文标题
将骗局放在上下文中：在黑手党游戏中识别欺骗性演员
Putting the Con in Context: Identifying Deceptive Actors in the Game of Mafia
论文作者
论文摘要
尽管神经网络表现出具有非凡的语言内容的非凡能力，但捕获与说话者对话作用相关的上下文信息是一个开放的研究领域。在这项工作中，我们通过黑手党的游戏分析了演讲者角色对语言使用的影响，其中参与者被分配了诚实或欺骗性的角色。除了建立一个框架以收集黑手党游戏记录数据集外，我们还证明了角色不同的玩家产生的语言存在差异。我们确认，分类模型能够将欺骗性的玩家排名为仅基于语言的使用而不是诚实的玩家。此外，我们表明，有关两个辅助任务的培训模型的表现优于标准的基于BERT的文本分类方法。我们还提出了使用训练有素的模型来识别区分玩家角色的功能的方法，这些功能可在黑手党游戏中用来帮助玩家。
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.
