Break index labeling of mandarin text via syntactic-to-prosodic tree mapping

Abstract

In this study, we investigate the break index labeling problem with a syntactic-to-prosodic structure conversion. The statistical relationship between the mapped syntactic tree structure and prosodic tree structure of sentences in the training set is used to generate a Synchronous Tree Substitution Grammar (STSG) which can describe the probabilistic mapping (substitution) rules between them. For a given test sentence and the corresponding parsed syntactic tree structure, thus generated STSG can convert the syntactic tree to a prosodic tree statistically. We compare the labeling results with other approaches and show the probabilistic mapping can indeed benefit break index labeling performance.