论文标题
建模非合作对话：理论和经验见解
Modeling Non-Cooperative Dialogue: Theoretical and Empirical Insights
论文作者
论文摘要
调查对话者的合作性在研究对话的语用学方面是核心。仅假设合作社的对话模型无法解释战略对话的动态。因此，我们研究了代理在完成同时进行视觉底盘任务时识别非合作对话者的能力。在这个新颖的环境中，我们研究了实现这一多任务目标的沟通策略的最佳性。我们使用学习理论的工具来开发一种理论模型来识别非合作对话者，并将该理论应用于分析不同的交流策略。我们还介绍了关于猜测中有关图像的非合作对话的语料库？ De Vries等人提出的数据集。 （2017）。在这种情况下，我们使用强化学习来实施多种交流策略，并发现经验结果证明了我们的理论。
Investigating cooperativity of interlocutors is central in studying pragmatics of dialogue. Models of conversation that only assume cooperative agents fail to explain the dynamics of strategic conversations. Thus, we investigate the ability of agents to identify non-cooperative interlocutors while completing a concurrent visual-dialogue task. Within this novel setting, we study the optimality of communication strategies for achieving this multi-task objective. We use the tools of learning theory to develop a theoretical model for identifying non-cooperative interlocutors and apply this theory to analyze different communication strategies. We also introduce a corpus of non-cooperative conversations about images in the GuessWhat?! dataset proposed by De Vries et al. (2017). We use reinforcement learning to implement multiple communication strategies in this context and find empirical results validate our theory.
