Learning Semantic Script Knowledge with Event Embeddings

Abstract

Induction of common sense knowledge about prototypical sequences of events has recently received much attention. 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 from texts. We show that this approach results in a substantial boost in ordering performance with respect to previous methods.

4 Pages, 1 figure, ICLR Workshop

References in corpus (1)

• Natural Language Processing (almost) from Scratch

Cited by in corpus (5)

• MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge
• Two Discourse Driven Language Models for Semantics
• Event Representation Learning Enhanced with External Commonsense Knowledge
• Automated Prediction of Temporal Relations
• Integrating Deep Event-Level and Script-Level Information for Script Event Prediction