Active participation in a conversation is key to building common ground, since understanding is jointly tailored by producers and recipients. Overhearers are deprived of the privilege of performing grounding acts and can only conjecture about intended meanings. Still, data generation and annotation, modelling, training and evaluation of NLP dialogue models place reliance on the overhearing paradigm. How much of the underlying grounding processes are thereby forfeited? As we show, there is evidence pointing to the impossibility of properly modelling human meta-communicative acts with data-driven learning models. In this paper, we discuss this issue and provide a preliminary analysis on the variability of human decisions for requesting clarification. Most importantly, we wish to bring this topic back to the community's table, encouraging discussion on the consequences of having models designed to only "listen in".

翻译：主动参与对话是建立共同理解的关键，因为理解是由说话者和接收者共同塑造的。偷听者失去了执行协同行为的特权，只能推测对方的意图。然而，NLP对话模型的数据生成与标注、建模、训练及评估均依赖于偷听范式。在此过程中，有多少潜在的协同过程因此被牺牲？如我们所示，有证据表明，基于数据驱动的学习模型无法恰当模拟人类的元交际行为。本文探讨这一问题，并就人类请求澄清决策的可变性提供初步分析。最重要的是，我们希望将这一议题重新带回学界讨论，鼓励探讨仅设计为“旁听”的模型所带来的后果。