%0 Conference Proceedings
%T Learning to Predict Denotational Probabilities For Modeling Entailment
%A Lai, Alice
%A Hockenmaier, Julia
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F lai-hockenmaier-2017-learning
%X We propose a framework that captures the denotational probabilities of words and phrases by embedding them in a vector space, and present a method to induce such an embedding from a dataset of denotational probabilities. We show that our model successfully predicts denotational probabilities for unseen phrases, and that its predictions are useful for textual entailment datasets such as SICK and SNLI.
%U https://aclanthology.org/E17-1068
%P 721-730