Inducing Neural Models of Script Knowledge
Ashutosh Modi, Ivan S. Titov · 2014
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 real-izations are computed based on distributed representations of predicates and their ar-guments, and then these representations are used to predict prototypical event or-derings. The parameters of the composi-tional process for computing the event rep-resentations and the ranking component of the model are jointly estimated. We show that this approach results in a sub-stantial boost in performance on the event ordering task with respect to the previous approaches, both on natural and crowd-sourced texts. 1