Grounded Agreement Games: Emphasizing Conversational Grounding in Visual Dialogue Settings
This addresses the issue of impoverished dialogue data in computational linguistics, particularly for visual dialogue settings, by introducing a method to encourage meta-semantic interaction, though it is incremental as it builds on existing frameworks.
The paper tackles the problem of current dialogue research focusing too narrowly on generating next utterances, which neglects conversational grounding. It proposes agreement games that add a constraint for mutual understanding, aiming to produce richer data for inducing better dialogue models.
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.