Paper: Improving Grammaticality in Statistical Sentence Generation: Introducing a Dependency Spanning Tree Algorithm with an Argument Satisfaction Model

ACL ID E09-1097
Title Improving Grammaticality in Statistical Sentence Generation: Introducing a Dependency Spanning Tree Algorithm with an Argument Satisfaction Model
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2009
Authors

Abstract-like text summarisation requires a means of producing novel summary sen- tences. In order to improve the grammati- cality of the generated sentence, we model a global (sentence) level syntactic struc- ture. We couch statistical sentence genera- tion as a spanning tree problem in order to search for the best dependency tree span- ning a set of chosen words. We also intro- duce a new search algorithm for this task that models argument satisfaction to im- prove the linguistic validity of the gener- ated tree. We treat the allocation of modi- fiers to heads as a weighted bipartite graph matching (or assignment) problem, a well studied problem in graph theory. Using BLEU to measure performance on a string regeneration task, we found an improve- ment, illustrating the benefit of the spa...