Finding Common Ground: Annotating and Predicting Common Ground in Spoken Conversations
This work addresses the challenge of understanding cognitive states in NLP for spoken dialog systems, though it is incremental as it builds on existing cognitive science concepts.
The paper tackles the problem of modeling common ground in spoken conversations by introducing a new annotation scheme and corpus, and conducts initial experiments to extract propositions and track their status from each speaker's perspective.
When we communicate with other humans, we do not simply generate a sequence of words. Rather, we use our cognitive state (beliefs, desires, intentions) and our model of the audience's cognitive state to create utterances that affect the audience's cognitive state in the intended manner. An important part of cognitive state is the common ground, which is the content the speaker believes, and the speaker believes the audience believes, and so on. While much attention has been paid to common ground in cognitive science, there has not been much work in natural language processing. In this paper, we introduce a new annotation and corpus to capture common ground. We then describe some initial experiments extracting propositions from dialog and tracking their status in the common ground from the perspective of each speaker.