Papers › Tracking the World State with Recurrent Entity Networks

Tracking the World State with Recurrent Entity Networks

12 Dec 2016arXiv:1612.03969archive 2025-07-28

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

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 representation of the state of the world as it receives new data. For language understanding 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 locations 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.

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parse_stories jimfleming/recurrent-entity-networks/entity_networks/prep_data.py community (archive-listed) unverified MIT (permissive) · 7c074f00ba51e7e9 · report
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Tasks

Procedural Text UnderstandingQuestion Answering

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Question Answering bAbi EntNet Accuracy (trained on 10k) 99.5% #3 of 14 Archive leaderboard report
Question Answering bAbi EntNet Accuracy (trained on 1k) 89.1% #3 of 14 Archive leaderboard report
Question Answering bAbi EntNet Mean Error Rate 9.7% #3 of 14 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Introduced by this paper: Recurrent Entity Network

Content-based AttentionLSTMLocation-based AttentionNeural Turing MachineRecurrent Entity NetworkSigmoid ActivationSoftmaxTanh Activation

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