Papers › End-To-End Memory Networks
End-To-End Memory Networks
Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, Rob Fergus
We introduce a neural network with a recurrent attention model over a possibly large external memory. The architecture is a form of Memory Network (Weston et al., 2015) but unlike the model in that work, it is trained end-to-end, and hence requires significantly less supervision during training, making it more generally applicable in realistic settings. It can also be seen as an extension of RNNsearch to the case where multiple computational steps (hops) are performed per output symbol. The flexibility of the model allows us to apply it to tasks as diverse as (synthetic) question answering and to language modeling. For the former our approach is competitive with Memory Networks, but with less supervision. For the latter, on the Penn TreeBank and Text8 datasets our approach demonstrates comparable performance to RNNs and LSTMs. In both cases we show that the key concept of multiple computational hops yields improved results.
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Code
Syntology Ran 2 of 15 code samples harvested from 4 repositories linked to this paper; 13 have no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · our draft was wrong.
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Code Syntology ran Syntology
15 samples harvested; 2 ran; 1 honoured the contract we drafted; 13 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Question Answering | bAbi | End-To-End Memory Networks | Accuracy (trained on 10k) | 93.4% | #6 of 14 | Archive leaderboard | report |
| Question Answering | bAbi | End-To-End Memory Networks | Accuracy (trained on 1k) | 86.1% | #6 of 14 | Archive leaderboard | report |
| Question Answering | bAbi | End-To-End Memory Networks | Mean Error Rate | 7.5% | #6 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: End-To-End Memory Network
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