Papers › Effective Approaches to Attention-based Neural Machine Translation

Effective Approaches to Attention-based Neural Machine Translation

17 Aug 2015EMNLP 2015 9arXiv:1508.04025archive 2025-07-28

Minh-Thang Luong, Hieu Pham, Christopher D. Manning

An attentional mechanism has lately been used to improve neural machine translation (NMT) by selectively focusing on parts of the source sentence during translation. However, there has been little work exploring useful architectures for attention-based NMT. This paper examines two simple and effective classes of attentional mechanism: a global approach which always attends to all source words and a local one that only looks at a subset of source words at a time. We demonstrate the effectiveness of both approaches over the WMT translation tasks between English and German in both directions. With local attention, we achieve a significant gain of 5.0 BLEU points over non-attentional systems which already incorporate known techniques such as dropout. Our ensemble model using different attention architectures has established a new state-of-the-art result in the WMT'15 English to German translation task with 25.9 BLEU points, an improvement of 1.0 BLEU points over the existing best system backed by NMT and an n-gram reranker.

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="1508.04025")

Code

Syntology Ran 2 of 8 code samples harvested from 1 repository linked to this paper; 6 have no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · our draft was wrong.

By repository: community (archive-listed): 6 samples from 1 repository, 0 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.

A-Jacobson/minimal-nmt mentioned on GitHubpytorchMIT report
CongBao/ChatBot mentioned on GitHub report
KevinYoung98/Seq2Seq-PyTorch mentioned on GitHubpytorch report
Shubham-SK/TreeOverAte mentioned on GitHubpytorch report
Shubham-SK/kronos mentioned on GitHubpytorch report
T9-LIN/MSc-Project mentioned on GitHubpytorch report
amitz25/PCCoder mentioned on GitHubpytorch report
b-etienne/Seq2seq-PyTorch mentioned on GitHubpytorch report
biyoml/End-to-End-Mandarin-ASR mentioned on GitHubpytorch report
biyoml/Pytorch-End-to-End-ASR-on-TIMIT mentioned on GitHubpytorch report
bplank/teaching-dl4nlp mentioned on GitHub report
chao-ji/tf-seq2seq mentioned on GitHubtf report
dalek7/attention mentioned on GitHubtf report
eaplatanios/symphony-mt mentioned on GitHubtfApache-2.0 report
hnt4499/seq2seq mentioned on GitHubpytorch report
huulinhcvp/chatBot mentioned on GitHubpytorch report
jackjhliu/End-to-End-Mandarin-ASR mentioned on GitHubpytorch report
jmyrberg/finnlem mentioned on GitHubtf report
khanhptnk/bandit-nmt mentioned on GitHubpytorch report
ldulcic/chatbot mentioned on GitHubpytorch report
mike-a-yen/date-translation mentioned on GitHubpytorch report
neqkir/attention-mechanism mentioned on GitHubtf report
pbcquoc/transformer mentioned on GitHub report
pcyin/pytorch_basic_nmt mentioned on GitHubpytorch report
philipperemy/keras-attention-mechanism mentioned on GitHubtfApache-2.0 report
prachetaphadnis/NMT_Seq2Seq_LexicalModel mentioned on GitHubpytorchMIT report
riversdie/chatbot mentioned on GitHubpytorchApache-2.0 report
saisandeep97/Chat-botV2 mentioned on GitHubpytorch report
say2sarwar/DeepAcid mentioned on GitHub report
sblayush/Tensorflow-Attention mentioned on GitHubtf report
sblayush/summarization mentioned on GitHubtf report
sen-pai/audio-word2vec-pytorch mentioned on GitHubpytorch report
shlokmehrotra/Convocare mentioned on GitHubpytorch report
sooftware/attentions mentioned on GitHubpytorchMIT report
vGkatsis/Chat_Bot_DL mentioned on GitHubpytorch report
vikua/keras-attention-models mentioned on GitHub 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

8 samples harvested; 2 ran; 1 honoured the contract we drafted; 6 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
6unverified

Licence: 2 of the 8 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 riversdie/chatbot. 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.

binaryMatrix riversdie/chatbot/dataload.py community (archive-listed) unverified Apache-2.0 (permissive) · b6b872f7d5584b69 · report
create_collate_fn riversdie/chatbot/dataload.py community (archive-listed) unverified Apache-2.0 (permissive) · 7cb03e0b055a9dc9 · report
generate riversdie/chatbot/train_eval.py community (archive-listed) unverified Apache-2.0 (permissive) · d69ad61c90b8b0e6 · report
maskNLLLoss riversdie/chatbot/train_eval.py community (archive-listed) unverified Apache-2.0 (permissive) · 191c9a860107527f · report
train_by_batch riversdie/chatbot/train_eval.py community (archive-listed) unverified Apache-2.0 (permissive) · 7b6afaedcd708676 · report
zeroPadding riversdie/chatbot/dataload.py community (archive-listed) unverified Apache-2.0 (permissive) · ca2ca3da0d6d55cf · report
count_parameters identical code first harvested elsewhere ran · honoured contract licence of this copy not recorded · 0f26a596e83c5565 · report
parse_arguments identical code first harvested elsewhere ran · our draft was wrong licence of this copy not recorded · 2a74b7a19b47b566 · report

Tasks

Image-guided Story Ending GenerationMachine TranslationNMTSentenceTranslation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image-guided Story Ending Generation LSMDC-E Seq2Seq BLEU-1 14.21 #4 of 4 Archive leaderboard report
Image-guided Story Ending Generation LSMDC-E Seq2Seq BLEU-2 4.56 #4 of 4 Archive leaderboard report
Image-guided Story Ending Generation LSMDC-E Seq2Seq BLEU-3 1.70 #4 of 4 Archive leaderboard report
Image-guided Story Ending Generation LSMDC-E Seq2Seq BLEU-4 0.70 #4 of 4 Archive leaderboard report
Image-guided Story Ending Generation LSMDC-E Seq2Seq CIDEr 8.69 #4 of 4 Archive leaderboard report
Image-guided Story Ending Generation LSMDC-E Seq2Seq METEOR 11.01 #4 of 4 Archive leaderboard report
Image-guided Story Ending Generation LSMDC-E Seq2Seq ROUGE-L 19.69 #4 of 4 Archive leaderboard report
Image-guided Story Ending Generation VIST-E Seq2Seq BLEU-1 13.96 #6 of 6 Archive leaderboard report
Image-guided Story Ending Generation VIST-E Seq2Seq BLEU-2 5.57 #6 of 6 Archive leaderboard report
Image-guided Story Ending Generation VIST-E Seq2Seq BLEU-3 2.94 #6 of 6 Archive leaderboard report
Image-guided Story Ending Generation VIST-E Seq2Seq BLEU-4 1.69 #6 of 6 Archive leaderboard report
Image-guided Story Ending Generation VIST-E Seq2Seq CIDEr 12.04 #6 of 6 Archive leaderboard report
Image-guided Story Ending Generation VIST-E Seq2Seq METEOR 4.54 #6 of 6 Archive leaderboard report
Image-guided Story Ending Generation VIST-E Seq2Seq ROUGE-L 16.84 #6 of 6 Archive leaderboard report
Machine Translation 20NEWS 12 Accuracy 1.0 #2 of 2 Archive leaderboard report
Machine Translation WMT2014 English-German RNN Enc-Dec Att BLEU score 20.9 #76 of 91 Archive leaderboard report
Machine Translation WMT2014 English-German Reverse RNN Enc-Dec BLEU score 14.0 #89 of 91 Archive leaderboard report
Machine Translation WMT2014 English-German RNN Enc-Dec BLEU score 11.3 #90 of 91 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

Location-based AttentionMultiplicative AttentionSoftmax

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