Learning to Predict Denotational Probabilities For Modeling Entailment

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.

[1]  Alice Lai,et al.  Illinois-LH: A Denotational and Distributional Approach to Semantics , 2014, *SEMEVAL.

[2]  Deriving Boolean structures from distributional vectors , 2015, Transactions of the Association for Computational Linguistics.

[3]  Hong Yu,et al.  Neural Tree Indexers for Text Understanding , 2016, EACL.

[4]  Phil Blunsom,et al.  Reasoning about Entailment with Neural Attention , 2015, ICLR.

[5]  Sanja Fidler,et al.  Order-Embeddings of Images and Language , 2015, ICLR.

[6]  Ido Dagan,et al.  The Third PASCAL Recognizing Textual Entailment Challenge , 2007, ACL-PASCAL@ACL.

[7]  Ido Dagan,et al.  Learning Entailment Relations by Global Graph Structure Optimization , 2012, CL.

[8]  Yang Liu,et al.  Learning Natural Language Inference using Bidirectional LSTM model and Inner-Attention , 2016, ArXiv.

[9]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[10]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[11]  Christopher Potts,et al.  A large annotated corpus for learning natural language inference , 2015, EMNLP.

[12]  Ido Dagan,et al.  Recognizing Textual Entailment: Models and Applications , 2013, Recognizing Textual Entailment: Models and Applications.

[13]  James Henderson,et al.  A Vector Space for Distributional Semantics for Entailment , 2016, ACL.

[14]  Jakob Uszkoreit,et al.  A Decomposable Attention Model for Natural Language Inference , 2016, EMNLP.

[15]  Hong Yu,et al.  Neural Semantic Encoders , 2016, EACL.

[16]  Andrew McCallum,et al.  Word Representations via Gaussian Embedding , 2014, ICLR.

[17]  Peter Young,et al.  From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions , 2014, TACL.

[18]  Katrin Erk,et al.  Representing words as regions in vector space , 2009, CoNLL.

[19]  Katrin Erk,et al.  Representing Meaning with a Combination of Logical Form and Vectors , 2015, ArXiv.

[20]  Marco Marelli,et al.  A SICK cure for the evaluation of compositional distributional semantic models , 2014, LREC.

[21]  Zhen-Hua Ling,et al.  Enhancing and Combining Sequential and Tree LSTM for Natural Language Inference , 2016, ArXiv.