Grounded Agreement Games: Emphasizing Conversational Grounding in Visual Dialogue Settings

By David Schlangen. 2019

16 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Multimodal Learning, Dialogue & Conversational AI

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Verena Rieser

Citations in the corpus

History for Visual Dialog: Do we really need it? (2020, background): “(De Vries et al., 2017) take place in a goal-oriented setting, which according to Schlangen (2019), will lead to data containing more natural dialog phenomena.”