---
title: "Who cites Effective Approaches to Attention-based Neural…"
description: "1,474 papers of the Elamur Research library cite «Effective Approaches to Attention-based Neural Machine Translation», found in their bibliographies ·…"
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---
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# Who cites «Effective Approaches to Attention-based Neural Machine Translation»

1,474 papers of our library cite it (of the 140,884 whose bibliographies we read) · 129 with a page here, the most cited first · page 1 of 2 · 1–100 · [← the paper](https://md.research.elamur.ai/free/en/papers/luong-2015-effective-approaches-attention-based-neural/)

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1. [**Attention Is All You Need**Ashish Vaswani, Noam Shazeer, Niki Parmar, et al.*2017 · 16,413 citations*](https://md.research.elamur.ai/free/en/papers/vaswani-2017-attention-all-need/)
2. [**A Comprehensive Survey on Graph Neural Networks**Zonghan Wu, Shirui Pan, Fengwen Chen, et al.*2019 · 10,295 citations*](https://md.research.elamur.ai/free/en/papers/wu-2019-comprehensive-survey-graph-neural-networks/)
3. [**A Call for Clarity in Reporting BLEU Scores**Matt Post*2018 · 4,130 citations*](https://md.research.elamur.ai/free/en/papers/post-2018-call-clarity-reporting-bleu-scores/)
4. [**Energy and Policy Considerations for Deep Learning in NLP**Emma Strubell, Ananya Ganesh, Andrew McCallum*2019 · 4,049 citations*](https://md.research.elamur.ai/free/en/papers/strubell-2019-energy-policy-considerations-deep-learning/)
5. [**fairseq: A Fast, Extensible Toolkit for Sequence Modeling**Myle Ott, Sergey Edunov, Alexei Baevski, et al.*2019 · 3,941 citations*](https://md.research.elamur.ai/free/en/papers/ott-2019-fairseq-fast-extensible-toolkit-sequence/)
6. [**Neural Machine Translation of Rare Words with Subword Units**Rico Sennrich, Barry Haddow, Alexandra Birch*2016 · 3,409 citations*](https://md.research.elamur.ai/free/en/papers/sennrich-2016-neural-machine-translation-rare-words/)
7. [**Convolutional Sequence to Sequence Learning**Jonas Gehring, Michael Auli, David Grangier, et al.*2017 · 2,983 citations*](https://md.research.elamur.ai/free/en/papers/gehring-2017-convolutional-sequence-sequence-learning/)
8. [**OpenNMT: Open-Source Toolkit for Neural Machine Translation**Guillaume Klein, Yoon Kim, Yuntian Deng, et al.*2017 · 2,460 citations*](https://md.research.elamur.ai/free/en/papers/klein-2017-opennmt-open-source-toolkit-neural/)
9. [**Relational inductive biases, deep learning, and graph networks**Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, et al.*2018 · 2,285 citations*](https://md.research.elamur.ai/free/en/papers/battaglia-2018-relational-inductive-biases-deep-learning/)
10. [**Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task**Tao Yu, Rui Zhang, Kai Yang, et al.*2018 · 1,582 citations*](https://md.research.elamur.ai/free/en/papers/yu-2018-spider-large-scale-human-labeled/)
11. [**Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments**Peter Anderson, Qi Wu, Damien Teney, et al.*2017 · 1,346 citations*](https://md.research.elamur.ai/free/en/papers/anderson-2017-vision-language-navigation-interpreting-visually/)
12. [**Deep Biaffine Attention for Neural Dependency Parsing**Timothy Dozat, Christopher D. Manning*2016 · 1,320 citations*](https://md.research.elamur.ai/free/en/papers/dozat-2016-deep-biaffine-attention-neural-dependency/)
13. [**DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset**Yanran Li, Hui Su, Xiaoyu Shen, et al.*2017 · 1,195 citations*](https://md.research.elamur.ai/free/en/papers/li-2017-dailydialog-manually-labelled-multi-turn/)
14. [**Aspect Level Sentiment Classification with Deep Memory Network**Duyu Tang, Bing Qin, Ting Liu*2016 · 1,187 citations*](https://md.research.elamur.ai/free/en/papers/tang-2016-aspect-level-sentiment-classification-deep/)
15. [**Abstractive Sentence Summarization with Attentive Recurrent Neural Networks**Sumit Chopra, Michael Auli, Alexander M. Rush*2016 · 1,085 citations*](https://md.research.elamur.ai/free/en/papers/chopra-2016-abstractive-sentence-summarization-attentive-recurrent/)
16. [**ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs**Wenpeng Yin, Hinrich Schütze, Bing Xiang, et al.*2016 · 1,048 citations*](https://md.research.elamur.ai/free/en/papers/yin-2016-abcnn-attention-based-convolutional-neural/)
17. [**Adversarial Learning for Neural Dialogue Generation**Jiwei Li, Will Monroe, Tianlin Shi, et al.*2017 · 1,044 citations*](https://md.research.elamur.ai/free/en/papers/li-2017-adversarial-learning-neural-dialogue-generation/)
18. [**A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents**Arman Cohan, Franck Dernoncourt, Doo Soon Kim, et al.*2018 · 1,018 citations*](https://md.research.elamur.ai/free/en/papers/cohan-2018-discourse-aware-attention-model-abstractive/)
19. [**Modeling Coverage for Neural Machine Translation**Zhaopeng Tu, Zhengdong Lu, Yang Liu, et al.*2016 · 927 citations*](https://md.research.elamur.ai/free/en/papers/tu-2016-modeling-coverage-neural-machine-translation/)
20. [**Learning to Ask: Neural Question Generation for Reading Comprehension**Xinya Du, Junru Shao, Claire Cardie*2017 · 913 citations*](https://md.research.elamur.ai/free/en/papers/du-2017-learning-ask-neural-question-generation/)
21. [**Is Attention Interpretable?**Sofia Serrano, Noah A. Smith*2019 · 885 citations*](https://md.research.elamur.ai/free/en/papers/serrano-2019-is-attention-interpretable/)
22. [**Are NLP Models really able to Solve Simple Math Word Problems?**Arkil Patel, Satwik Bhattamishra, Navin Goyal*2021 · 882 citations*](https://md.research.elamur.ai/free/en/papers/patel-2021-are-nlp-models-really-able/)
23. [**Challenges in Data-to-Document Generation**Sam Wiseman, Stuart M. Shieber, Alexander M. Rush*2017 · 846 citations*](https://md.research.elamur.ai/free/en/papers/wiseman-2017-challenges-data-document-generation/)
24. [**Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism**Orhan Firat, Kyunghyun Cho, Yoshua Bengio*2016 · 821 citations*](https://md.research.elamur.ai/free/en/papers/firat-2016-multi-way-multilingual-neural-machine/)
25. [**Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting**Yen-Chun Chen, Mohit Bansal*2018 · 819 citations*](https://md.research.elamur.ai/free/en/papers/chen-2018-fast-abstractive-summarization-reinforce-selected/)
26. [**Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks**Chen Zhang, Qiuchi Li, Dawei Song*2019 · 807 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2019-aspect-based-sentiment-classification-aspect/)
27. [**Learning Deep Transformer Models for Machine Translation**Qiang Wang, Bei Li, Tong Xiao, et al.*2019 · 798 citations*](https://md.research.elamur.ai/free/en/papers/wang-2019-learning-deep-transformer-models-machine/)
28. [**Non-Autoregressive Neural Machine Translation**Jiatao Gu, James Bradbury, Caiming Xiong, et al.*2017 · 765 citations*](https://md.research.elamur.ai/free/en/papers/gu-2017-non-autoregressive-neural-machine-translation/)
29. [**Unified Pre-training for Program Understanding and Generation**Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, et al.*2021 · 746 citations*](https://md.research.elamur.ai/free/en/papers/ahmad-2021-unified-pre-training-program-understanding/)
30. [**ProgPrompt: Generating Situated Robot Task Plans using Large Language Models**Ishika Singh, Valts Blukis, Arsalan Mousavian, et al.*2022 · 710 citations*](https://md.research.elamur.ai/free/en/papers/singh-2022-progprompt-generating-situated-robot-task/)
31. [**The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations**Felix Hill, Antoine Bordes, Sumit Chopra, et al.*2015 · 693 citations*](https://md.research.elamur.ai/free/en/papers/hill-2015-goldilocks-principle-reading-children-s/)
32. [**A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task**Danqi Chen, Jason Bolton, Christopher D. Manning*2016 · 689 citations*](https://md.research.elamur.ai/free/en/papers/chen-2016-thorough-examination-cnn-daily-mail/)
33. [**Efficient Content-Based Sparse Attention with Routing Transformers**Aurko Roy, Mohammad Saffar, Ashish Vaswani, et al.*2020 · 660 citations*](https://md.research.elamur.ai/free/en/papers/roy-2020-efficient-content-based-sparse-attention/)
34. [**Transformation Networks for Target-Oriented Sentiment Classification**Xin Li, Lidong Bing, Wai Lam, et al.*2018 · 594 citations*](https://md.research.elamur.ai/free/en/papers/li-2018-transformation-networks-target-oriented-sentiment/)
35. [**QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension**Adams Wei Yu, David Dohan, Minh-Thang Luong, et al.*2018 · 581 citations*](https://md.research.elamur.ai/free/en/papers/yu-2018-qanet-combining-local-convolution-global/)
36. [**Dynamic Coattention Networks For Question Answering**Caiming Xiong, Victor Zhong, Richard Socher*2016 · 578 citations*](https://md.research.elamur.ai/free/en/papers/xiong-2016-dynamic-coattention-networks-question-answering/)
37. [**Minimum Risk Training for Neural Machine Translation**Shiqi Shen, Yong Cheng, Zhongjun He, et al.*2015 · 572 citations*](https://md.research.elamur.ai/free/en/papers/shen-2015-minimum-risk-training-neural-machine/)
38. [**Sentence Simplification with Deep Reinforcement Learning**Xingxing Zhang, Mirella Lapata*2017 · 567 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2017-sentence-simplification-deep-reinforcement-learning/)
39. [**Massive Exploration of Neural Machine Translation Architectures**Denny Britz, Anna Goldie, Minh-Thang Luong, et al.*2017 · 562 citations*](https://md.research.elamur.ai/free/en/papers/britz-2017-massive-exploration-neural-machine-translation/)
40. [**Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates**Taku Kudo*2018 · 561 citations*](https://md.research.elamur.ai/free/en/papers/kudo-2018-subword-regularization-improving-neural-network/)
41. [**Style Transfer Through Back-Translation**Shrimai Prabhumoye, Yulia Tsvetkov, Ruslan Salakhutdinov, et al.*2018 · 554 citations*](https://md.research.elamur.ai/free/en/papers/prabhumoye-2018-style-transfer-through-back-translation/)
42. [**Fully Character-Level Neural Machine Translation without Explicit Segmentation**Jason Lee, Kyunghyun Cho, Thomas Hofmann*2017 · 545 citations*](https://md.research.elamur.ai/free/en/papers/lee-2017-fully-character-level-neural-machine/)
43. [**The Natural Language Decathlon: Multitask Learning as Question Answering**Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, et al.*2018 · 523 citations*](https://md.research.elamur.ai/free/en/papers/mccann-2018-natural-language-decathlon-multitask-learning/)
44. [**Deep Learning Scaling is Predictable, Empirically**Joel Hestness, Sharan Narang, Newsha Ardalani, et al.*2017 · 497 citations*](https://md.research.elamur.ai/free/en/papers/hestness-2017-deep-learning-scaling-predictable-empirically/)
45. [**Semi-Supervised Sequence Modeling with Cross-View Training**Kevin Clark, Minh-Thang Luong, Christopher D. Manning, et al.*2018 · 489 citations*](https://md.research.elamur.ai/free/en/papers/clark-2018-semi-supervised-sequence-modeling-cross/)
46. [**A Convolutional Encoder Model for Neural Machine Translation**Jonas Gehring, Michael Auli, David Grangier, et al.*2017 · 487 citations*](https://md.research.elamur.ai/free/en/papers/gehring-2017-convolutional-encoder-model-neural-machine/)
47. [**Regularizing Neural Networks by Penalizing Confident Output Distributions**Gabriel Pereyra, George Tucker, Jan Chorowski, et al.*2017 · 482 citations*](https://md.research.elamur.ai/free/en/papers/pereyra-2017-regularizing-neural-networks-penalizing-confident/)
48. [**Learned in Translation: Contextualized Word Vectors**Bryan McCann, James Bradbury, Caiming Xiong, et al.*2017 · 481 citations*](https://md.research.elamur.ai/free/en/papers/mccann-2017-learned-translation-contextualized-word-vectors/)
49. [**Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models**Minh-Thang Luong, Christopher D. Manning*2016 · 469 citations*](https://md.research.elamur.ai/free/en/papers/luong-2016-achieving-open-vocabulary-neural-machine/)
50. [**Graph-to-Sequence Learning using Gated Graph Neural Networks**Daniel Beck, Gholamreza Haffari, Trevor Cohn*2018 · 456 citations*](https://md.research.elamur.ai/free/en/papers/beck-2018-graph-sequence-learning-gated-graph/)
51. [**MuST-C: a Multilingual Speech Translation Corpus**Mattia A. Di Gangi, Roldano Cattoni, Luisa Bentivogli, et al.*2019 · 436 citations*](https://md.research.elamur.ai/free/en/papers/gangi-2019-must-c-multilingual-speech-translation/)
52. [**Neural versus Phrase-Based Machine Translation Quality: a Case Study**Luisa Bentivogli, Arianna Bisazza, Mauro Cettolo, et al.*2016 · 436 citations*](https://md.research.elamur.ai/free/en/papers/bentivogli-2016-neural-versus-phrase-based-machine/)
53. [**Multi-Source Neural Translation**Barret Zoph, Kevin Knight*2016 · 432 citations*](https://md.research.elamur.ai/free/en/papers/zoph-2016-multi-source-neural-translation/)
54. [**Deep Communicating Agents for Abstractive Summarization**Asli Celikyilmaz, Antoine Bosselut, Xiaodong He, et al.*2018 · 431 citations*](https://md.research.elamur.ai/free/en/papers/celikyilmaz-2018-deep-communicating-agents-abstractive-summarization/)
55. [**Neural AMR: Sequence-to-Sequence Models for Parsing and Generation**Ioannis Konstas, Srinivasan Iyer, Mark Yatskar, et al.*2017 · 425 citations*](https://md.research.elamur.ai/free/en/papers/konstas-2017-neural-amr-sequence-sequence-models/)
56. [**Controllable Abstractive Summarization**Angela Fan, David Grangier, Michael Auli*2018 · 423 citations*](https://md.research.elamur.ai/free/en/papers/fan-2018-controllable-abstractive-summarization/)
57. [**Data Augmentation for Low-Resource Neural Machine Translation**Marzieh Fadaee, Arianna Bisazza, Christof Monz*2017 · 419 citations*](https://md.research.elamur.ai/free/en/papers/fadaee-2017-data-augmentation-low-resource-neural/)
58. [**Pay Less Attention with Lightweight and Dynamic Convolutions**Felix Wu, Angela Fan, Alexei Baevski, et al.*2019 · 416 citations*](https://md.research.elamur.ai/free/en/papers/wu-2019-pay-less-attention-lightweight-dynamic/)
59. [**Are Sixteen Heads Really Better than One?**Paul Michel, Omer Levy, Graham Neubig*2019 · 412 citations*](https://md.research.elamur.ai/free/en/papers/michel-2019-sixteen-heads-really-better-than/)
60. [**Unsupervised Neural Machine Translation**Mikel Artetxe, Gorka Labaka, Eneko Agirre, et al.*2017 · 410 citations*](https://md.research.elamur.ai/free/en/papers/artetxe-2017-unsupervised-neural-machine-translation/)
61. [**Exploiting Source-side Monolingual Data in Neural Machine Translation**Jiajun Zhang, Chengqing Zong*2016 · 408 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2016-exploiting-source-side-monolingual-data/)
62. [**A Character-level Decoder without Explicit Segmentation for Neural Machine Translation**Junyoung Chung, Kyunghyun Cho, Yoshua Bengio*2016 · 407 citations*](https://md.research.elamur.ai/free/en/papers/chung-2016-character-level-decoder-without-explicit/)
63. [**Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems**Andrea Madotto, Chien-Sheng Wu, Pascale Fung*2018 · 401 citations*](https://md.research.elamur.ai/free/en/papers/madotto-2018-mem2seq-effectively-incorporating-knowledge-bases/)
64. [**Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder**Thanh-Le Ha, Jan Niehues, Alex Waibel*2016 · 398 citations*](https://md.research.elamur.ai/free/en/papers/ha-2016-multilingual-neural-machine-translation-universal/)
65. [**A Unified Generative Framework for Aspect-based Sentiment Analysis**Hang Yan, Junqi Dai, Tuo Ji, et al.*2021 · 396 citations*](https://md.research.elamur.ai/free/en/papers/yan-2021-unified-generative-framework-aspect-based/)
66. [**A Unified Generative Framework for Various NER Subtasks**Hang Yan, Tao Gui, Junqi Dai, et al.*2021 · 392 citations*](https://md.research.elamur.ai/free/en/papers/yan-2021-unified-generative-framework-various-ner/)
67. [**Cross-lingual Transfer Learning for Multilingual Task Oriented Dialog**Sebastian Schuster, Sonal Gupta, Rushin Shah, et al.*2019 · 390 citations*](https://md.research.elamur.ai/free/en/papers/schuster-2019-cross-lingual-transfer-learning-multilingual/)
68. [**Deep Semantic Role Labeling with Self-Attention**Zhixing Tan, Mingxuan Wang, Jun Xie, et al.*2017 · 384 citations*](https://md.research.elamur.ai/free/en/papers/tan-2017-deep-semantic-role-labeling-self/)
69. [**Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks**Yao Zhao, Xiaochuan Ni, Yuanyuan Ding, et al.*2018 · 376 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2018-paragraph-level-neural-question-generation/)
70. [**An Analysis of Encoder Representations in Transformer-Based Machine Translation**Alessandro Raganato, Jörg Tiedemann*2018 · 366 citations*](https://md.research.elamur.ai/free/en/papers/raganato-2018-analysis-encoder-representations-transformer-based/)
71. [**Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring**Fei Dong, Yue Zhang, Jie Yang*2017 · 365 citations*](https://md.research.elamur.ai/free/en/papers/dong-2017-attention-based-recurrent-convolutional-neural/)
72. [**Exploring Neural Text Simplification Models**Sergiu Nisioi, Sanja Štajner, Simone Paolo Ponzetto, et al.*2017 · 353 citations*](https://md.research.elamur.ai/free/en/papers/nisioi-2017-exploring-neural-text-simplification-models/)
73. [**Zero-Resource Translation with Multi-Lingual Neural Machine Translation**Orhan Firat, Baskaran Sankaran, Yaser Al-onaizan, et al.*2016 · 350 citations*](https://md.research.elamur.ai/free/en/papers/firat-2016-zero-resource-translation-multi-lingual/)
74. [**Reformulating Unsupervised Style Transfer as Paraphrase Generation**Kalpesh Krishna, John Wieting, Mohit Iyyer*2020 · 348 citations*](https://md.research.elamur.ai/free/en/papers/krishna-2020-reformulating-unsupervised-style-transfer-as/)
75. [**Controlling Output Length in Neural Encoder-Decoders**Yuta Kikuchi, Graham Neubig, Ryohei Sasano, et al.*2016 · 323 citations*](https://md.research.elamur.ai/free/en/papers/kikuchi-2016-controlling-output-length-neural-encoder/)
76. [**Paraphrasing Revisited with Neural Machine Translation**Jonathan Mallinson, Rico Sennrich, Mirella Lapata*2017 · 319 citations*](https://md.research.elamur.ai/free/en/papers/mallinson-2017-paraphrasing-revisited-neural-machine-translation/)
77. [**Multi-task Sequence to Sequence Learning**Minh-Thang Luong, Quoc V. Le, Ilya Sutskever, et al.*2015 · 313 citations*](https://md.research.elamur.ai/free/en/papers/luong-2015-multi-task-sequence-sequence-learning/)
78. [**Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow**Pengcheng Yin, Bowen Deng, Edgar Chen, et al.*2018 · 306 citations*](https://md.research.elamur.ai/free/en/papers/yin-2018-learning-mine-aligned-code-natural/)
79. [**Globally Coherent Text Generation with Neural Checklist Models**Chloé Kiddon, Luke Zettlemoyer, Yejin Choi*2016 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kiddon-2016-globally-coherent-text-generation-neural/)
80. [**Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation**Ning Dai, Jianze Liang, Xipeng Qiu, et al.*2019 · 300 citations*](https://md.research.elamur.ai/free/en/papers/dai-2019-style-transformer-unpaired-text-style/)
81. [**Incorporating Discrete Translation Lexicons into Neural Machine Translation**Philip Arthur, Graham Neubig, Satoshi Nakamura*2016 · 298 citations*](https://md.research.elamur.ai/free/en/papers/arthur-2016-incorporating-discrete-translation-lexicons-into/)
82. [**Exploiting Cross-Sentence Context for Neural Machine Translation**Longyue Wang, Zhaopeng Tu, Andy Way, et al.*2017 · 291 citations*](https://md.research.elamur.ai/free/en/papers/wang-2017-exploiting-cross-sentence-context-neural/)
83. [**SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation**Xinyi Wang, Hieu Pham, Zihang Dai, et al.*2018 · 287 citations*](https://md.research.elamur.ai/free/en/papers/wang-2018-switchout-efficient-data-augmentation-algorithm/)
84. [**Transfer Learning across Low-Resource, Related Languages for Neural Machine Translation**Toan Q. Nguyen, David Chiang*2017 · 285 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2017-transfer-learning-across-low-resource/)
85. [**Text Generation from Knowledge Graphs with Graph Transformers**Rik Koncel-Kedziorski, Dhanush Bekal, Yi Luan, et al.*2019 · 284 citations*](https://md.research.elamur.ai/free/en/papers/kedziorski-2019-text-generation-knowledge-graphs-graph/)
86. [**Learning Neural Templates for Text Generation**Sam Wiseman, Stuart Shieber, Alexander Rush*2018 · 279 citations*](https://md.research.elamur.ai/free/en/papers/wiseman-2018-learning-neural-templates-text-generation/)
87. [**MIME: MIMicking Emotions for Empathetic Response Generation**Navonil Majumder, Pengfei Hong, Shanshan Peng, et al.*2020 · 277 citations*](https://md.research.elamur.ai/free/en/papers/majumder-2020-mime-mimicking-emotions-empathetic-response/)
88. [**Adaptively Sparse Transformers**Gonçalo M. Correia, Vlad Niculae, André F. T. Martins*2019 · 270 citations*](https://md.research.elamur.ai/free/en/papers/correia-2019-adaptively-sparse-transformers/)
89. [**On Extractive and Abstractive Neural Document Summarization with Transformer Language Models**Jonathan Pilault, Raymond Li, Sandeep Subramanian, et al.*2020 · 259 citations*](https://md.research.elamur.ai/free/en/papers/pilault-2020-extractive-abstractive-neural-document-summarization/)
90. [**Reasoning with Sarcasm by Reading In-Between**Yi Tay, Anh Tuan Luu, Siu Cheung Hui, et al.*2018 · 247 citations*](https://md.research.elamur.ai/free/en/papers/tay-2018-reasoning-sarcasm-reading-between/)
91. [**Training Neural Machine Translation to Apply Terminology Constraints**Georgiana Dinu, Prashant Mathur, Marcello Federico, et al.*2019 · 242 citations*](https://md.research.elamur.ai/free/en/papers/dinu-2019-training-neural-machine-translation-apply/)
92. [**Document Context Neural Machine Translation with Memory Networks**Sameen Maruf, Gholamreza Haffari*2018 · 236 citations*](https://md.research.elamur.ai/free/en/papers/maruf-2018-document-context-neural-machine-translation/)
93. [**Classical Structured Prediction Losses for Sequence to Sequence Learning**Sergey Edunov, Myle Ott, Michael Auli, et al.*2018 · 232 citations*](https://md.research.elamur.ai/free/en/papers/edunov-2018-classical-structured-prediction-losses-sequence/)
94. [**Learning to Parse and Translate Improves Neural Machine Translation**Akiko Eriguchi, Yoshimasa Tsuruoka, Kyunghyun Cho*2017 · 214 citations*](https://md.research.elamur.ai/free/en/papers/eriguchi-2017-learning-parse-translate-improves-neural/)
95. [**Data-Driven Sentence Simplification: Survey and Benchmark**Fernando Alva-Manchego, Carolina Scarton, Lucia Specia*2020 · 213 citations*](https://md.research.elamur.ai/free/en/papers/manchego-2020-data-driven-sentence-simplification-survey/)
96. [**Morphological Inflection Generation with Hard Monotonic Attention**Roee Aharoni, Yoav Goldberg*2017 · 208 citations*](https://md.research.elamur.ai/free/en/papers/aharoni-2017-morphological-inflection-generation-hard-monotonic/)
97. [**Analyzing Uncertainty in Neural Machine Translation**Myle Ott, Michael Auli, David Grangier, et al.*2018 · 190 citations*](https://md.research.elamur.ai/free/en/papers/ott-2018-analyzing-uncertainty-neural-machine-translation/)
98. [**A Dataset for Multi-Target Stance Detection**Parinaz Sobhani, Diana Inkpen, Xiaodan Zhu*2017 · 190 citations*](https://md.research.elamur.ai/free/en/papers/sobhani-2017-dataset-multi-target-stance-detection/)
99. [**Graph Convolutional Encoders for Syntax-aware Neural Machine Translation**Jasmijn Bastings, Ivan Titov, Wilker Aziz, et al.*2017 · 190 citations*](https://md.research.elamur.ai/free/en/papers/bastings-2017-graph-convolutional-encoders-syntax-aware/)
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