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。

基于共识的对话游戏：强调视觉对话环境中的交流基础