Papers › End-To-End Memory Networks

End-To-End Memory Networks

31 Mar 2015NeurIPS 2015 12arXiv:1503.08895archive 2025-07-28

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.

PaperPDFConference PDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="1503.08895")

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.

By repository: community (archive-listed): 13 samples from 4 repositories, 2 ran; 2 identical to code first harvested elsewhere. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

44 repositories listed; official and paper-mentioned ones first.

facebook/MemNN officialmentioned in papermentioned on GitHubtorchNOASSERTION report
HSabbar/Question-Answering mentioned on GitHubtf report
Monireh2/kg-deductive-reasoner mentioned on GitHubNOASSERTION report
RAGHAVJHA01/chatboat mentioned on GitHub report
Saurabh2798/Q-A-bot mentioned on GitHub report
SeonbeomKim/TensorFlow-MemN2N mentioned on GitHubtf report
TDeepanshPandey/Chat_Bot mentioned on GitHub report
TommyWongww/MemoryNetworks mentioned on GitHubpytorch report
candoz/memn2n-dialog-babi-keras mentioned on GitHubtf report
cstghitpku/GateMemN2N mentioned on GitHubtfMIT report
dare0021/MemN2N_Bench mentioned on GitHubMIT report
domluna/memn2n mentioned on GitHubtf report
facebook/MazeBase mentioned on GitHubtorchNOASSERTION report
facebook/bAbI-tasks mentioned on GitHubtorchNOASSERTION report
facebookarchive/MazeBase mentioned on GitHubtorchNOASSERTION report
facebookarchive/babi-tasks mentioned on GitHubtorchNOASSERTION report
gaurav9713/simpleQA_chatbot mentioned on GitHub report
google/neural-logic-machines mentioned on GitHubpytorchApache-2.0 report
gryan12/chat-bot mentioned on GitHubtf report
ishalyminov/memn2n mentioned on GitHubtf report
jojonki/MemoryNetworks mentioned on GitHubpytorch report
mustafa-yavuz/chatbot-qa mentioned on GitHub report
nbansal90/bAbi_QA mentioned on GitHub report
qapitan/babi-marcus mentioned on GitHubtorchNOASSERTION report
rakeshbm/QA-using-Torch mentioned on GitHubtorch report
sayakbanerjee1999/Chat-Bot mentioned on GitHub report
sheryl-ai/MemGCN mentioned on GitHubtf report
simonjisu/E2EMN mentioned on GitHubpytorch report
stikbuf/Language_Modeling mentioned on GitHubtf report
thomlake/pytorch-attention mentioned on GitHubpytorchBSD-2-Clause report
uwnlp/qrn mentioned on GitHubtfMIT report
vinhkhuc/MemN2N-babi-python mentioned on GitHubtfNOASSERTION report
wujsAct/DeepLearningModels mentioned on GitHubtf report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

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.

1ran · honoured contract
1ran · our draft was wrong
13unverified

Licence: 5 of the 15 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from 4 repositories linked to this paper, official or community; each sample names its own and says which. Some samples are identical code Syntology first harvested from another repository; for those, this paper's copy is not located and its licence is not recorded. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

position_encoding ishalyminov/memn2n/memn2n/memn2n.py community (archive-listed) ran · honoured contract fingerprinted MIT (permissive) · b0ca12d8cc81c8be · report
toNumpy SeonbeomKim/TensorFlow-MemN2N/training.py community (archive-listed) ran · our draft was wrong no licence file found · pointer only · f0dbdbccfb2bbdd5 · report
get_stories nbansal90/bAbi_QA/memnn.py community (archive-listed) unverified no licence file found · pointer only · 67aee3b89ae06d5a · report
get_stories dare0021/MemN2N_Bench/data_process.py community (archive-listed) unverified MIT (permissive) · 02143fa734f99d53 · report
get_stories dare0021/MemN2N_Bench/src/babi_baseline.py community (archive-listed) unverified MIT (permissive) · 6a417e8bf87cd8fa · report
parse_stories nbansal90/bAbi_QA/memnn.py community (archive-listed) unverified no licence file found · pointer only · 0753ec8f5509e983 · report
parse_stories dare0021/MemN2N_Bench/data_process.py community (archive-listed) unverified MIT (permissive) · e94421d72e63a32e · report
parse_stories dare0021/MemN2N_Bench/kerasTeam.py community (archive-listed) unverified MIT (permissive) · 86ebfb268b5ff1d1 · report
parse_stories dare0021/MemN2N_Bench/src/babi_baseline.py community (archive-listed) unverified MIT (permissive) · 31d3313b31e0ce05 · report
parse_stories dare0021/MemN2N_Bench/src/single_layer_v5.py community (archive-listed) unverified MIT (permissive) · d59d30f527e3ff33 · report
tokenize dare0021/MemN2N_Bench/data_process.py community (archive-listed) unverified MIT (permissive) · a8a4fc29963cfdd9 · report
tokenize dare0021/MemN2N_Bench/kerasTeam.py community (archive-listed) unverified MIT (permissive) · dbf39005cc117d57 · report
tokenize dare0021/MemN2N_Bench/src/single_layer_v5.py community (archive-listed) unverified MIT (permissive) · 8ec1ed5d99153049 · report
parse_stories identical code first harvested elsewhere unverified licence of this copy not recorded · 9d142ef2a5e220be · report
tokenize identical code first harvested elsewhere unverified licence of this copy not recorded · 6d27ce4e6c139f45 · report

Tasks

Language ModelingLanguage ModellingQuestion Answering

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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

End-To-End Memory NetworkSoftmax

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections