Estimation of discourse segmentation labels from crowd data

Abstract

For annotation tasks involving independent judgments, probabilistic models have been used to infer ground truth labels from data where a crowd of many annotators labels the same items. Such models have been shown to produce results superior to taking the majority vote, but have not been applied to sequential data. We present two methods to infer ground truth labels from sequential annotations where we assume judgments are not independent, based on the observation that an annotator's segments all tend to be several utterances long. The data consists of crowd labels for annotation of discourse segment boundaries. The new methods extend Hidden Markov Models to relax the independence assumption. The two methods are distinct, so positive labels proposed by both are taken to be ground truth. In addition, results of the models are checked using metrics that test whether an annotator's accuracy relative to a given model remains consistent across different conversations.