Conversational AI as Improvisational Co-Creation – A Dialogic Perspective

Nancy Fulda, Chaz Gundry

Abstract:

Dialogue is often modeled as an encoder-decoder problem:
incoming utterances are translated into a computational representation of their semantic meaning, passed through a transition function to obtain a response, and then passed through
a decoder to render the response as natural language. This
view, while computationally appealing, omits the role of human emotions, mental state, and shared world knowledge in
conversation. We challenge this viewpoint by recasting the
task of dialogue modeling as a two-party co-creative process in which symbolic and subsymbolic knowledge representations are combined to inform response selection. Symbolic knowledge is identified and extracted from conversational text in real-time and used to create a shared symbolic
representation of the user, the agent, and their respective relationships to objects and abstract concepts within the larger
world. As part of this process, the agent takes on an “identity”
which it has largely constructed as a result of the stochasticity in its own response patterns, but to which it subsequently
adheres. This emergent identity becomes a critical aspect of
the system’s future behavior, and helps to evoke a more natural, human-centric flavor in automated conversational frameworks.