Paper: A Hierarchical Bayesian Model for Unsupervised Induction of Script Knowledge

ACL ID E14-1006
Title A Hierarchical Bayesian Model for Unsupervised Induction of Script Knowledge
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

Scripts representing common sense knowledge about stereotyped sequences of events have been shown to be a valu- able resource for NLP applications. We present a hierarchical Bayesian model for unsupervised learning of script knowledge from crowdsourced descriptions of human activities. Events and constraints on event ordering are induced jointly in one unified framework. We use a statistical model over permutations which captures event ordering constraints in a more flexible way than previous approaches. In order to alleviate the sparsity problem caused by using relatively small datasets, we incorporate in our hierarchical model an informed prior on word distributions. The resulting model substantially outperforms a state-of-the-art method on the event ordering task.