Recognizing Authority in Dialogue with an Integer Linear Programming Constrained Model

Elijah Mayfield, Carolyn Penstein Rosé

Abstract: We present a novel computational formulation of speaker authority in discourse. This notion, which focuses on how speakers position themselves relative to each other in discourse, is first developed into a reliable coding scheme (0.71 agreement between human annotators). We also provide a computational model for automatically annotating text using this coding scheme, using supervised learning enhanced by constraints implemented with Integer Linear Programming. We show that this constrained model's analyses of speaker authority correlates very strongly with expert human judgments (r2 coefficient of 0.947).