将骗局置于情境中:识别黑手党游戏中的欺骗行为者

Samee Ibraheem, G. Zhou, John DeNero
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引用次数: 4

摘要

虽然神经网络在模拟语言内容方面表现出非凡的能力,但捕捉与说话者会话角色相关的上下文信息是一个开放的研究领域。在这项工作中,我们通过黑手党游戏来分析说话者角色对语言使用的影响,在该游戏中,参与者被分配为诚实或欺骗的角色。除了建立一个框架来收集黑手党游戏记录的数据集之外,我们还证明了不同角色的玩家所产生的语言存在差异。我们确认,分类模型能够根据欺骗玩家的语言使用情况,将他们评为比诚实玩家更可疑的玩家。此外,我们证明了两个辅助任务的训练模型优于标准的基于bert的文本分类方法。我们还提出了使用我们训练过的模型来识别区分玩家角色的特征的方法,这些特征可以用于在黑手党游戏中帮助玩家。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Putting the Con in Context: Identifying Deceptive Actors in the Game of Mafia
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.
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