Learning to Predict Denotational Probabilities For Modeling Entailment

Alice Lai, Julia Hockenmaier


Abstract
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.
Anthology ID:
E17-1068
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
721–730
Language:
URL:
https://aclanthology.org/E17-1068
DOI:
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/E17-1068.pdf
Data
SICKSNLI