Aligning Predicate Argument Structures in Monolingual Comparable Texts: A New Corpus for a New Task

Discourse coherence is an important aspect of natural language that is still understudied in computational linguistics. Our aim is to learn factors that constitute coherent discourse from data, with a focus on how to realize predicateargument structures (PAS) in a model that exceeds the sentence level. In particular, we aim to study the case of non-realized arguments as a coherence inducing factor. This task can be broken down into two subtasks. The first aligns predicates across comparable texts, admitting partial argument structure correspondence. The resulting alignments and their contexts can then be used for developing a coherence model for argument realization. This paper introduces a large corpus of comparable monolingual texts as a prerequisite for approaching this task, including an evaluation set with manual predicate alignments. We illustrate the potential of this new resource for the empirical investigation of discourse coherence phenomena. Initial experiments on the task of predicting predicate alignments across text pairs show promising results. Our findings establish that manual and automatic predicate alignments across texts are feasible and that our data set holds potential for empirical research into a variety of discourse-related tasks.