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.

本研究通过研究推理游戏中的发言者角色，分析发言者角色对语言使用的影响，验证了基于语言使用的分类模型可以准确区分诚实玩家和欺骗玩家，提出了使用辅助任务训练模型的方法，以及基于训练模型识别玩家角色的特征以协助推理游戏的方法。

把 “骗” 放在环境中：在 “黑手党” 游戏中识别欺诈演员

Putting the Con in Context: Identifying Deceptive Actors in the Game of Mafia

Spoken language understanding (SLU) is an essential component in
conversational systems. Most SLU component treats each utterance independently,
and then the following components aggregate the multi-turn information in the
separate phases. In order to avoid error propagation and effectively utilize
contexts, prior work leveraged history for contextual SLU. However, the
previous model only paid attention to the content in history utterances without
considering their temporal information and speaker roles. In the dialogues, the
most recent utterances should be more important than the least recent ones.
Furthermore, users usually pay attention to 1) self history for reasoning and
2) others' utterances for listening, the speaker of the utterances may provides
informative cues to help understanding. Therefore, this paper proposes an
attention-based network that additionally leverages temporal information and
speaker role for better SLU, where the attention to contexts and speaker roles
can be automatically learned in an end-to-end manner. The experiments on the
benchmark Dialogue State Tracking Challenge 4 (DSTC4) dataset show that the
time-aware dynamic role attention networks significantly improve the
understanding performance.

该论文提出了一种基于注意力机制的网络，利用上下文和说话人角色进行时态信息的学习和自适应特征提取，从而显著提高了口语理解的性能。