This work examines the relationships between several measures of linguistic alignment in task-based group conversation, and assesses how useful these measures are for predicting task performance and participant affect. The study is carried out using human-human conversational data, with potential implications for human-AI conversations where an artificial agent can decide if and how to align itself linguistically with human subjects. We implement several alignment measures including long-term measures that assess the level of convergence over the course of the conversation, as well as short-term coordination measures that have been related in previous research to power dynamics. The study uses two publicly available English-language survival task datasets. After analyzing correlations between the various linguistic alignment measures, we perform clustering in order to unveil the main types of alignment patterns that are prevalent in the data. Finally, we use the alignment measures as machine learning features to predict participant task performance and participant affect.
Article ID: 2022L25
Publisher: Canadian Artificial Intelligence Association