Analyzing Team Performance with Embeddings from Multiparty Dialogues
This work addresses team performance prediction for applications like conversational agents, but it is incremental as it builds on existing embedding methods with specific features.
This paper tackled the problem of predicting team performance from embeddings learned from multiparty dialogues, finding that dialogue act and sentiment embeddings effectively classify performance across teamwork phases, unlike syntactic entrainment.
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.