title: Tracking the World State with Recurrent Entity Networks
abstract: We introduce a new model, the Recurrent Entity Network (EntNet). It is equipped
  with a dynamic long-term memory which allows it to maintain and update a rep-
  resentation of the state of the world as it receives new data. For language under-
  standing tasks, it can reason on-the-fly as it reads text, not just when it is required
  to answer a question or respond as is the case for a Memory Network (Sukhbaatar
  et al., 2015). Like a Neural Turing Machine or Differentiable Neural Computer
  (Graves et al., 2014; 2016) it maintains a fixed size memory and can learn to
  perform location and content-based read and write operations. However, unlike
  those models it has a simple parallel architecture in which several memory loca-
  tions can be updated simultaneously. The EntNet sets a new state-of-the-art on
  the bAbI tasks, and is the first method to solve all the tasks in the 10k training
  examples setting. We also demonstrate that it can solve a reasoning task which
  requires a large number of supporting facts, which other methods are not able to
  solve, and can generalize past its training horizon. It can also be practically used
  on large scale datasets such as Children’s Book Test, where it obtains competitive
  performance, reading the story in a single pass.
authors: Mikael Henaff Jason Weston Arthur Szlam Antoine Bordes Yann LeCun
hIndex: Yann LeCun 120