Fine-Grained Analysis of Team Collaborative Dialogue
Ian Perera Matthew Johnson Carson Wilber
Natural language analysis of human collaborative chat dialogues is an
understudied domain with many unique challenges: a large number of dialogue act
labels, underspecified and dynamic tasks, interleaved topics, and long-range
contextual dependence. While prior work has studied broad metrics of team
dialogue and associated performance using methods such as LSA, there has been
little effort in generating fine-grained descriptions of team dynamics and
individual performance from dialogue. We describe initial work towards
developing an explainable analytics tool in the software development domain
using Slack chats mined from our organization, including generation of a novel,
hierarchical labeling scheme; design of descriptive metrics based on the
frequency of occurrence of dialogue acts; and initial results using a
transformer + CRF architecture to incorporate long-range context.