Tracking the World State with Recurrent Entity Networks

Mikael Henaff, Jason Weston, Arthur Szlam, Antoine Bordes, Yann LeCun

Nov 04, 2016 (modified: Mar 03, 2017) ICLR 2017 conference submission readers: everyone
  • 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.
  • TL;DR: A new memory-augmented model which learns to track the world state, obtaining SOTA on the bAbI tasks amongst other results.
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  • Keywords: Natural language processing, Deep learning