Common grounding is the process of creating and maintaining mutual
understandings, which is a critical aspect of sophisticated human
communication. While various task settings have been proposed in existing
literature, they mostly focus on creating common ground under static context
and ignore the aspect of maintaining them overtime under dynamic context. In
this work, we propose a novel task setting to study the ability of both
creating and maintaining common ground in dynamic environments. Based on our
minimal task formulation, we collected a large-scale dataset of 5,617 dialogues
to enable fine-grained evaluation and analysis of various dialogue systems.
Through our dataset analyses, we highlight novel challenges introduced in our
setting, such as the usage of complex spatio-temporal expressions to create and
maintain common ground. Finally, we conduct extensive experiments to assess the
capabilities of our baseline dialogue system and discuss future prospects of
our research.

本研究提出了一个新颖的任务设定，以研究在动态环境下创建和维护共同基础的能力，通过新的数据集和实验，探讨了该设定引入的挑战和基线对话系统的能力，并讨论了未来的研究前景。

在动态环境中维护共同基础

Maintaining Common Ground in Dynamic Environments

Where early work on dialogue in Computational Linguistics put much emphasis
on dialogue structure and its relation to the mental states of the dialogue
participants (e.g., Allen 1979, Grosz & Sidner 1986), current work mostly
reduces dialogue to the task of producing at any one time a next utterance;
e.g. in neural chatbot or Visual Dialogue settings. As a methodological
decision, this is sound: Even the longest journey is a sequence of steps. It
becomes detrimental, however, when the tasks and datasets from which dialogue
behaviour is to be learned are tailored too much to this framing of the
problem. In this short note, we describe a family of settings which still allow
to keep dialogues simple, but add a constraint that makes participants care
about reaching mutual understanding. In such agreement games, there is a
secondary, but explicit goal besides the task level goal, and that is to reach
mutual understanding about whether the task level goal has been reached. As we
argue, this naturally triggers meta-semantic interaction and mutual engagement,
and hence leads to richer data from which to induce models.

本文介绍了一种通过协议达成共识的对话设置，除任务级别目标外，还有一个次要的、显式的目的 —— 达成对任务级别目标是否达成的共识 —— 使得会话参与者更关心彼此的理解，从而产生更丰富的数据以推导模型。