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

Liwen Zhang, John Winn, Ryota Tomioka

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.

Conflicts: uchicago.edu, microsoft.com

Keywords: Natural language processing, Supervised Learning, Deep learning