title: Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering
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.
authors: Liwen Zhang John Winn Ryota Tomioka
hIndex: John Winn 35