Inducing Neural Models of Script Knowledge

Ashutosh Modi, Ivan Titov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Induction of common sense knowledge about prototypical sequence of events has recently received much attention (e.g., Chambers and Jurafsky (2008); Regneri et al. (2010)). Instead of inducing this knowledge in the form of graphs, as in much of the previous work, in our method, distributed representations of event realizations are computed based on distributed representations of predicates and their arguments, and then these representations are used to predict prototypical event orderings. The parameters of the compositional process for computing the event representations and the ranking component of the model are jointly estimated. We show that this approach results in a substantial boost in performance on the event ordering task with respect to the previous approaches, both on natural and crowdsourced texts.
Original languageEnglish
Title of host publicationProceedings of the Eighteenth Conference on Computational Natural Language Learning, CoNLL 2014, Baltimore, Maryland, USA, June 26-27, 2014
Pages49-57
Number of pages9
Publication statusPublished - 2014

Cite this