Aligning Predicates across Monolingual Comparable Texts using Graph-based Clustering

Generating coherent discourse is an important aspect in natural language generation. Our aim is to learn factors that constitute coherent discourse from data, with a focus on how to realize predicate-argument structures in a model that exceeds the sentence level. We present an important subtask for this overall goal, in which we align predicates across comparable texts, admitting partial argument structure correspondence. The contribution of this work is two-fold: We first construct a large corpus resource of comparable texts, including an evaluation set with manual predicate alignments. Secondly, we present a novel approach for aligning predicates across comparable texts using graph-based clustering with Mincuts. Our method significantly outperforms other alignment techniques when applied to this novel alignment task, by a margin of at least 6.5 percentage points in F1-score.