  Good communication is indubitably the foundation of effective teamwork. Over
time teams develop their own communication styles and often exhibit
entrainment, a conversational phenomena in which humans synchronize their
linguistic choices. This paper examines the problem of predicting team
performance from embeddings learned from multiparty dialogues such that teams
with similar conflict scores lie close to one another in vector space.
Embeddings were extracted from three types of features: 1) dialogue acts 2)
sentiment polarity 3) syntactic entrainment. Although all of these features can
be used to effectively predict team performance, their utility varies by the
teamwork phase. We separate the dialogues of players playing a cooperative game
into stages: 1) early (knowledge building) 2) middle (problem-solving) and 3)
late (culmination). Unlike syntactic entrainment, both dialogue act and
sentiment embeddings are effective for classifying team performance, even
during the initial phase. This finding has potential ramifications for the
development of conversational agents that facilitate teaming.
