---
title: "Who cites Sequence to Sequence Learning with Neural Networks"
description: "3,346 papers of the Elamur Research library cite «Sequence to Sequence Learning with Neural Networks», found in their bibliographies…"
canonical: "https://research.elamur.ai/free/en/papers/sutskever-2014-sequence-sequence-learning-neural-networks/cited-by/"
---
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# Who cites «Sequence to Sequence Learning with Neural Networks»

3,346 papers of our library cite it (of the 149,439 whose bibliographies we read) · 445 with a page here, the most cited first · page 1 of 5 · 1–100 · [← back to «Sequence to Sequence Learning with Neural Networks»](https://md.research.elamur.ai/free/en/papers/sutskever-2014-sequence-sequence-learning-neural-networks/)

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1. [**Neural Machine Translation by Jointly Learning to Align and Translate arXiv:1409.0473**Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio*2014 · 19,287 citations*](https://md.research.elamur.ai/free/en/papers/bahdanau-2014-neural-machine-translation-jointly-learning/)
2. [**Attention Is All You Need arXiv:1706.03762**Ashish Vaswani, Noam Shazeer, Niki Parmar, et al.*2017 · 17,458 citations*](https://md.research.elamur.ai/free/en/papers/vaswani-2017-attention-all-need/)
3. [**Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer arXiv:1910.10683**Colin Raffel, Noam Shazeer, Adam Roberts, et al.*2019 · 11,325 citations*](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/)
4. **[Effective Approaches to Attention-based Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/luong-2015-effective-approaches-attention-based-neural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Thang Luong, Hieu Pham, Christopher D. Manning*2015 · 9,718 citations*
5. [**TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems arXiv:1603.04467**Martín Abadi, Ashish Agarwal, Paul Barham, et al.*2016 · 9,512 citations*](https://md.research.elamur.ai/free/en/papers/abadi-2016-tensorflow-large-scale-machine-learning/)
6. [**On the Properties of Neural Machine Translation: Encoder-Decoder Approaches arXiv:1409.1259**Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, et al.*2014 · 7,803 citations*](https://md.research.elamur.ai/free/en/papers/cho-2014-properties-neural-machine-translation-encoder/)
7. [**Show, Attend and Tell: Neural Image Caption Generation with Visual Attention arXiv:1502.03044**Kelvin Xu, Jimmy Ba, Ryan Kiros, et al.*2015 · 7,670 citations*](https://md.research.elamur.ai/free/en/papers/xu-2015-show-attend-tell-neural-image/)
8. [**Representation Learning with Contrastive Predictive Coding arXiv:1807.03748**Aaron van den Oord, Yazhe Li, Oriol Vinyals*2018 · 7,022 citations*](https://md.research.elamur.ai/free/en/papers/oord-2018-representation-learning-contrastive-predictive-coding/)
9. **[Survey of Hallucination in Natural Language Generation](https://md.research.elamur.ai/free/en/papers/ji-2022-survey-hallucination-natural-language-generation/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Ziwei Ji, Nayeon Lee, Rita Frieske, et al.*2022 · 5,974 citations*
10. [**Neural Architecture Search with Reinforcement Learning arXiv:1611.01578**Barret Zoph, Quoc V. Le*2016 · 4,413 citations*](https://md.research.elamur.ai/free/en/papers/zoph-2016-neural-architecture-search-reinforcement-learning/)
11. **[Neural Machine Translation of Rare Words with Subword Units](https://md.research.elamur.ai/free/en/papers/sennrich-2016-neural-machine-translation-rare-words/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Rico Sennrich, Barry Haddow, Alexandra Birch*2016 · 4,067 citations*
12. [**Longformer: The Long-Document Transformer arXiv:2004.05150**Iz Beltagy, Matthew E. Peters, Arman Cohan*2020 · 3,880 citations*](https://md.research.elamur.ai/free/en/papers/beltagy-2020-longformer-long-document-transformer/)
13. [**Teaching Machines to Read and Comprehend arXiv:1506.03340**Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, et al.*2015 · 3,858 citations*](https://md.research.elamur.ai/free/en/papers/hermann-2015-teaching-machines-read-comprehend/)
14. [**Training Verifiers to Solve Math Word Problems arXiv:2110.14168**Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, et al.*2021 · 3,796 citations*](https://md.research.elamur.ai/free/en/papers/cobbe-2021-training-verifiers-solve-math-word/)
15. **[A Neural Attention Model for Abstractive Sentence Summarization](https://md.research.elamur.ai/free/en/papers/rush-2015-neural-attention-model-abstractive-sentence/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Alexander M. Rush, Sumit Chopra, Jason Weston*2015 · 3,390 citations*
16. [**Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books arXiv:1506.06724**Yukun Zhu, Ryan Kiros, Richard Zemel, et al.*2015 · 3,083 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2015-aligning-books-movies-towards-story/)
17. [**Gemini: A Family of Highly Capable Multimodal Models arXiv:2312.11805**Gemini Team, Rohan Anil, Sebastian Borgeaud, et al.*2023 · 3,050 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2023-gemini-family-highly-capable-multimodal/)
18. [**Convolutional Sequence to Sequence Learning arXiv:1705.03122**Jonas Gehring, Michael Auli, David Grangier, et al.*2017 · 3,042 citations*](https://md.research.elamur.ai/free/en/papers/gehring-2017-convolutional-sequence-sequence-learning/)
19. **[CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://md.research.elamur.ai/free/en/papers/feng-2020-codebert-pre-trained-model-programming/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhangyin Feng, Daya Guo, Duyu Tang, et al.*2020 · 2,967 citations*
20. **[A Diversity-Promoting Objective Function for Neural Conversation Models](https://md.research.elamur.ai/free/en/papers/li-2016-diversity-promoting-objective-function-neural/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Jiwei Li, Michel Galley, Chris Brockett, et al.*2016 · 2,960 citations*
21. [**Evaluating Large Language Models Trained on Code arXiv:2107.03374**Mark Chen, Jerry Tworek, Heewoo Jun, et al.*2021 · 2,659 citations*](https://md.research.elamur.ai/free/en/papers/chen-2021-evaluating-large-language-models-trained/)
22. [**Robust Speech Recognition via Large-Scale Weak Supervision arXiv:2212.04356**Alec Radford, Jong Wook Kim, Tao Xu, et al.*2022 · 2,546 citations*](https://md.research.elamur.ai/free/en/papers/radford-2022-robust-speech-recognition-large-scale/)
23. **[OpenNMT: Open-Source Toolkit for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/klein-2017-opennmt-open-source-toolkit-neural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guillaume Klein, Yoon Kim, Yuntian Deng, et al.*2017 · 2,475 citations*
24. **[Generating Sentences from a Continuous Space](https://md.research.elamur.ai/free/en/papers/bowman-2016-generating-sentences-continuous-space/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, et al.*2016 · 2,435 citations*
25. [**Recurrent Neural Network Regularization arXiv:1409.2329**Wojciech Zaremba, Ilya Sutskever, Oriol Vinyals*2014 · 2,412 citations*](https://md.research.elamur.ai/free/en/papers/zaremba-2014-recurrent-neural-network-regularization/)
26. [**Measuring Mathematical Problem Solving With the MATH Dataset arXiv:2103.03874**Dan Hendrycks, Collin Burns, Saurav Kadavath, et al.*2021 · 2,395 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2021-measuring-mathematical-problem-solving-math/)
27. **[Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning](https://md.research.elamur.ai/free/en/papers/sharma-2018-conceptual-captions-cleaned-hypernymed-image/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Piyush Sharma, Nan Ding, Sebastian Goodman, et al.*2018 · 2,348 citations*
28. [**Deep Speech 2: End-to-End Speech Recognition in English and Mandarin arXiv:1512.02595**Dario Amodei, Rishita Anubhai, Eric Battenberg, et al.*2015 · 2,341 citations*](https://md.research.elamur.ai/free/en/papers/amodei-2015-deep-speech-2-end-end/)
29. [**Relational inductive biases, deep learning, and graph networks arXiv:1806.01261**Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, et al.*2018 · 2,335 citations*](https://md.research.elamur.ai/free/en/papers/battaglia-2018-relational-inductive-biases-deep-learning/)
30. **[Stacked Attention Networks for Image Question Answering](https://md.research.elamur.ai/free/en/papers/yang-2015-stacked-attention-networks-image-question/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Zichao Yang, Xiaodong He, Jianfeng Gao, et al.*2015 · 2,157 citations*
31. **[BLEURT: Learning Robust Metrics for Text Generation](https://md.research.elamur.ai/free/en/papers/sellam-2020-bleurt-learning-robust-metrics-text/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Thibault Sellam, Dipanjan Das, Ankur Parikh*2020 · 2,032 citations*
32. **[Incorporating Copying Mechanism in Sequence-to-Sequence Learning](https://md.research.elamur.ai/free/en/papers/gu-2016-incorporating-copying-mechanism-sequence-sequence/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiatao Gu, Zhengdong Lu, Hang Li, et al.*2016 · 1,915 citations*
33. **[Character-Aware Neural Language Models](https://md.research.elamur.ai/free/en/papers/kim-2015-character-aware-neural-language-models/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Yoon Kim, Yacine Jernite, David Sontag, et al.*2015 · 1,874 citations*
34. **[On Faithfulness and Factuality in Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/maynez-2020-faithfulness-factuality-abstractive-summarization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Joshua Maynez, Shashi Narayan, Bernd Bohnet, et al.*2020 · 1,857 citations*
35. **[Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://md.research.elamur.ai/free/en/papers/zhang-2018-personalizing-dialogue-agents-i-have/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Saizheng Zhang, Emily Dinan, Jack Urbanek, et al.*2018 · 1,854 citations*
36. [**PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization arXiv:1912.08777**Jingqing Zhang, Yao Zhao, Mohammad Saleh, et al.*2019 · 1,828 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2019-pegasus-pre-training-extracted-gap/)
37. [**Layer Normalization arXiv:1607.06450**Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton*2016 · 1,819 citations*](https://md.research.elamur.ai/free/en/papers/ba-2016-layer-normalization/)
38. [**Attention-Based Models for Speech Recognition arXiv:1506.07503**Jan Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk, et al.*2015 · 1,819 citations*](https://md.research.elamur.ai/free/en/papers/chorowski-2015-attention-based-models-speech-recognition/)
39. [**A Neural Conversational Model arXiv:1506.05869**Oriol Vinyals, Quoc Le*2015 · 1,791 citations*](https://md.research.elamur.ai/free/en/papers/vinyals-2015-neural-conversational-model/)
40. [**Pre-trained Models for Natural Language Processing: A Survey arXiv:2003.08271 · SCIENCE CHINA Technological Sciences,2020, 63, 1872-1897**Xipeng Qiu, Tianxiang Sun, Yige Xu, et al.*2020 · 1,759 citations*](https://md.research.elamur.ai/free/en/papers/qiu-2020-pre-trained-models-natural-language/)
41. [**Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj**Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.*2022 · 1,756 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/)
42. **[Document Modeling with Gated Recurrent Neural Network for Sentiment Classification](https://md.research.elamur.ai/free/en/papers/tang-2015-document-modeling-gated-recurrent-neural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Duyu Tang, Bing Qin, Ting Liu*2015 · 1,686 citations*
43. **[MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://md.research.elamur.ai/free/en/papers/budzianowski-2018-multiwoz-large-scale-multi-domain/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Paweł Budzianowski, Tsung-Hsien Wen, Bo-Hsiang Tseng, et al.*2018 · 1,663 citations*
44. [**A Deep Reinforced Model for Abstractive Summarization arXiv:1705.04304**Romain Paulus, Caiming Xiong, Richard Socher*2017 · 1,657 citations*](https://md.research.elamur.ai/free/en/papers/paulus-2017-deep-reinforced-model-abstractive-summarization/)
45. **[Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://md.research.elamur.ai/free/en/papers/yu-2018-spider-large-scale-human-labeled/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tao Yu, Rui Zhang, Kai Yang, et al.*2018 · 1,602 citations*
46. [**Deep Speech: Scaling up end-to-end speech recognition arXiv:1412.5567**Awni Hannun, Carl Case, Jared Casper, et al.*2014 · 1,600 citations*](https://md.research.elamur.ai/free/en/papers/hannun-2014-deep-speech-scaling-up-end/)
47. **[Deep Reinforcement Learning for Dialogue Generation](https://md.research.elamur.ai/free/en/papers/li-2016-deep-reinforcement-learning-dialogue-generation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiwei Li, Will Monroe, Alan Ritter, et al.*2016 · 1,502 citations*
48. **[Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects](https://md.research.elamur.ai/free/en/papers/ni-2019-justifying-recommendations-distantly-labeled-reviews/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jianmo Ni, Jiacheng Li, Julian McAuley*2019 · 1,435 citations*
49. **[Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments](https://md.research.elamur.ai/free/en/papers/anderson-2017-vision-language-navigation-interpreting-visually/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Peter Anderson, Qi Wu, Damien Teney, et al.*2017 · 1,414 citations*
50. **[Contextual String Embeddings for Sequence Labeling](https://md.research.elamur.ai/free/en/papers/akbik-2018-contextual-string-embeddings-sequence-labeling/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Alan Akbik, Duncan Blythe, Roland Vollgraf*2018 · 1,400 citations*
51. [**Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models arXiv:1411.2539**Ryan Kiros, Ruslan Salakhutdinov, Richard S. Zemel*2014 · 1,371 citations*](https://md.research.elamur.ai/free/en/papers/kiros-2014-unifying-visual-semantic-embeddings-multimodal/)
52. [**Sequence Level Training with Recurrent Neural Networks arXiv:1511.06732**Marc'Aurelio Ranzato, Sumit Chopra, Michael Auli, et al.*2015 · 1,370 citations*](https://md.research.elamur.ai/free/en/papers/ranzato-2015-sequence-level-training-recurrent-neural/)
53. **[Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://md.research.elamur.ai/free/en/papers/reimers-2020-making-monolingual-sentence-embeddings-multilingual/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Nils Reimers, Iryna Gurevych*2020 · 1,322 citations*
54. **[COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://md.research.elamur.ai/free/en/papers/bosselut-2019-comet-commonsense-transformers-automatic-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Antoine Bosselut, Hannah Rashkin, Maarten Sap, et al.*2019 · 1,294 citations*
55. [**CTRL: A Conditional Transformer Language Model for Controllable Generation arXiv:1909.05858**Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, et al.*2019 · 1,291 citations*](https://md.research.elamur.ai/free/en/papers/keskar-2019-ctrl-conditional-transformer-language-model/)
56. **[Neural Responding Machine for Short-Text Conversation](https://md.research.elamur.ai/free/en/papers/shang-2015-neural-responding-machine-short-text/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lifeng Shang, Zhengdong Lu, Hang Li*2015 · 1,257 citations*
57. **[Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems](https://md.research.elamur.ai/free/en/papers/wen-2015-semantically-conditioned-lstm-based-natural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tsung-Hsien Wen, Milica Gašić, Nikola Mrkšić, et al.*2015 · 1,202 citations*
58. **[Aspect Level Sentiment Classification with Deep Memory Network](https://md.research.elamur.ai/free/en/papers/tang-2016-aspect-level-sentiment-classification-deep/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Duyu Tang, Bing Qin, Ting Liu*2016 · 1,197 citations*
59. [**Matching Networks for One Shot Learning arXiv:1606.04080**Oriol Vinyals, Charles Blundell, Timothy Lillicrap, et al.*2016 · 1,197 citations*](https://md.research.elamur.ai/free/en/papers/vinyals-2016-matching-networks-one-shot-learning/)
60. [**MS MARCO: A Human Generated MAchine Reading COmprehension Dataset arXiv:1611.09268**Payal Bajaj, Daniel Campos, Nick Craswell, et al.*2016 · 1,138 citations*](https://md.research.elamur.ai/free/en/papers/bajaj-2016-ms-marco-human-generated/)
61. **[Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling](https://md.research.elamur.ai/free/en/papers/marcheggiani-2017-encoding-sentences-graph-convolutional-networks/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Diego Marcheggiani, Ivan Titov*2017 · 1,106 citations*
62. **[Very Deep Convolutional Networks for Text Classification](https://md.research.elamur.ai/free/en/papers/conneau-2017-very-deep-convolutional-networks-text/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Alexis Conneau, Holger Schwenk, Loïc Barrault, et al.*2017 · 1,094 citations*
63. [**Wizard of Wikipedia: Knowledge-Powered Conversational agents arXiv:1811.01241**Emily Dinan, Stephen Roller, Kurt Shuster, et al.*2018 · 1,067 citations*](https://md.research.elamur.ai/free/en/papers/dinan-2018-wizard-wikipedia-knowledge-powered-conversational/)
64. **[Adversarial Learning for Neural Dialogue Generation](https://md.research.elamur.ai/free/en/papers/li-2017-adversarial-learning-neural-dialogue-generation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiwei Li, Will Monroe, Tianlin Shi, et al.*2017 · 1,063 citations*
65. **[What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties](https://md.research.elamur.ai/free/en/papers/conneau-2018-what-you-can-cram-into/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Alexis Conneau, German Kruszewski, Guillaume Lample, et al.*2018 · 1,045 citations*
66. [**The NarrativeQA Reading Comprehension Challenge**Tomáš Kočiský, Jonathan Schwarz, Phil Blunsom, et al.*2018 · 1,042 citations*](https://md.research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/)
67. [**A Theoretically Grounded Application of Dropout in Recurrent Neural Networks arXiv:1512.05287**Yarin Gal, Zoubin Ghahramani*2015 · 1,032 citations*](https://md.research.elamur.ai/free/en/papers/gal-2015-theoretically-grounded-application-dropout-recurrent/)
68. **[A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents](https://md.research.elamur.ai/free/en/papers/cohan-2018-discourse-aware-attention-model-abstractive/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Arman Cohan, Franck Dernoncourt, Doo Soon Kim, et al.*2018 · 1,026 citations*
69. **[Big Bird: Transformers for Longer Sequences](https://md.research.elamur.ai/free/en/papers/zaheer-2020-big-bird-transformers-longer-sequences/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Manzil Zaheer, Guru Guruganesh, Avinava Dubey, et al.*2020 · 1,023 citations*
70. **[On Using Very Large Target Vocabulary for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/jean-2015-very-large-target-vocabulary-neural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sébastien Jean, Kyunghyun Cho, Roland Memisevic, et al.*2015 · 1,013 citations*
71. **[Neural Summarization by Extracting Sentences and Words](https://md.research.elamur.ai/free/en/papers/cheng-2016-neural-summarization-extracting-sentences-words/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jianpeng Cheng, Mirella Lapata*2016 · 1,011 citations*
72. [**Visualizing and Understanding Recurrent Networks arXiv:1506.02078**Andrej Karpathy, Justin Johnson, Li Fei-Fei*2015 · 954 citations*](https://md.research.elamur.ai/free/en/papers/karpathy-2015-visualizing-understanding-recurrent-networks/)
73. **[Visual7W: Grounded Question Answering in Images](https://md.research.elamur.ai/free/en/papers/zhu-2015-visual7w-grounded-question-answering-images/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Yuke Zhu, Oliver Groth, Michael Bernstein, et al.*2015 · 952 citations*
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