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".

参与主动对话对于建立共同基础至关重要，因为理解是由发出者和接收者共同塑造的；偷听者无法执行基础行为，只能猜测意图，数据生成、注释、建模、训练和评估自然语言处理对话模型仍依靠这种偷听范式，然而，正如我们展示的那样，有证据表明无法通过数据驱动的学习模型来正确地建模人类元交际行为，本文讨论了这个问题，并对人类在请求澄清方面的决策变化进行了初步分析，最重要的是，我们希望将这个主题重新提到社区的议程中，鼓励关于仅设计为 “偷听” 的模型后果的讨论。