{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sequence-to-sequence-learning-with-neural","title":"Sequence to Sequence Learning with Neural Networks","arxiv_id":"1409.3215","date":"2014-09-10","proceeding":"NeurIPS 2014 12","authors":["Ilya Sutskever","Oriol Vinyals","Quoc V. Le"],"abstract":"Deep Neural Networks (DNNs) are powerful models that have achieved excellent\nperformance on difficult learning tasks. Although DNNs work well whenever large\nlabeled training sets are available, they cannot be used to map sequences to\nsequences. In this paper, we present a general end-to-end approach to sequence\nlearning that makes minimal assumptions on the sequence structure. Our method\nuses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to\na vector of a fixed dimensionality, and then another deep LSTM to decode the\ntarget sequence from the vector. Our main result is that on an English to\nFrench translation task from the WMT'14 dataset, the translations produced by\nthe LSTM achieve a BLEU score of 34.8 on the entire test set, where the LSTM's\nBLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did\nnot have difficulty on long sentences. For comparison, a phrase-based SMT\nsystem achieves a BLEU score of 33.3 on the same dataset. When we used the LSTM\nto rerank the 1000 hypotheses produced by the aforementioned SMT system, its\nBLEU score increases to 36.5, which is close to the previous best result on\nthis task. The LSTM also learned sensible phrase and sentence representations\nthat are sensitive to word order and are relatively invariant to the active and\nthe passive voice. Finally, we found that reversing the order of the words in\nall source sentences (but not target sentences) improved the LSTM's performance\nmarkedly, because doing so introduced many short term dependencies between the\nsource and the target sentence which made the optimization problem easier.","url_abs":"http://arxiv.org/abs/1409.3215v3","url_pdf":"http://arxiv.org/pdf/1409.3215v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"sequence-to-sequence-learning-with-neural","repo_url":"https://github.com/2023-MindSpore-1/ms-code-197","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mindspore","reach":{"status":"ok"}},{"paper_slug":"sequence-to-sequence-learning-with-neural","repo_url":"https://github.com/2023-MindSpore-1/ms-code-198","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mindspore","reach":{"status":"ok"}},{"paper_slug":"sequence-to-sequence-learning-with-neural","repo_url":"https://github.com/2023-MindSpore-1/ms-code-200","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mindspore","reach":{"status":"ok"}},{"paper_slug":"sequence-to-sequence-learning-with-neural","repo_url":"https://github.com/2023-MindSpore-1/ms-code-61","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mindspore","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"sequence-to-sequence-learning-with-neural","repo_url":"https://github.com/2023-MindSpore-1/ms-code-62","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mindspore","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"sequence-to-sequence-learning-with-neural","repo_url":"https://github.com/AMNAALMGLY/NLP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"sequence-to-sequence-learning-with-neural","repo_url":"https://github.com/ArushiSinghal/NMT-assignment1","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"sequence-to-sequence-learning-with-neural","repo_url":"https://github.com/ArushiSinghal/Neural-Machine-Translation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"sequence-to-sequence-learning-with-neural","repo_url":"https://github.com/ArushiSinghal/Neural-Machine-Translation-English-Hindi-for-domain-data","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"sequence-to-sequence-learning-with-neural","repo_url":"https://github.com/CGCL-codes/naturalcc","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"sequence-to-sequence-learning-with-neural","repo_url":"https://github.com/Chatha-Sphere/pno-ai","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"sequence-to-sequence-learning-with-neural","repo_url":"https://github.com/EuphoriaYan/ChatRobot-For-Keras2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"sequence-to-sequence-learning-with-neural","repo_url":"https://github.com/Guillem96/pointer-nn-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"sequence-to-sequence-learning-with-neural","repo_url":"https://github.com/IvanVassi/Seq2Seq-w-attention-dialogue-model","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"sequence-to-sequence-learning-with-neural","repo_url":"https://github.com/JRC1995/Abstractive-Summarization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"sequence-to-sequence-learning-with-neural","repo_url":"https://github.com/Mind23-2/MindCode-73","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mindspore","reach":{"status":"ok"}},{"paper_slug":"sequence-to-sequence-learning-with-neural","repo_url":"https://github.com/NehaTamore/abstractive-text-summarizer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"sequence-to-sequence-learning-with-neural","repo_url":"https://github.com/TEAMLAB-Lecture/deep_nlp_101","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"sequence-to-sequence-learning-with-neural","repo_url":"https://github.com/VishalFun/Keras_practice","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"sequence-to-sequence-learning-with-neural","repo_url":"https://github.com/VladimirV99/Automatic-Translation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"sequence-to-sequence-learning-with-neural","repo_url":"https://github.com/a1085728420/seq2seq-mindspore","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mindspore","reach":{"s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