Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering

Liwen Zhang, John Winn, Ryota Tomioka

Nov 04, 2016 (modified: Nov 30, 2016) ICLR 2017 conference submission readers: everyone
  • Abstract: We propose the Gaussian attention model for content-based neural memory access. With the proposed attention model, a neural network has the additional degree of freedom to control the focus of its attention from a laser sharp attention to a broad attention. It is applicable whenever we can assume that the distance in the latent space reflects some notion of semantics. We use the proposed attention model as a scoring function for the embedding of a knowledge base into a continuous vector space and then train a model that performs question answering about the entities in the knowledge base. The proposed attention model can handle both the propagation of uncertainty when following a series of relations and also the conjunction of conditions in a natural way. On a dataset of soccer players who participated in the FIFA World Cup 2014, we demonstrate that our model can handle both path queries and conjunctive queries well.
  • TL;DR: We make (simple) knowledge base queries differentiable using the Gaussian attention model.
  • Keywords: Natural language processing, Supervised Learning, Deep learning
  • Conflicts: uchicago.edu, microsoft.com

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