---
title: "Who cites Greg S. Corrado"
description: "8,086 papers of the Elamur Research library cite the works of Greg S. Corrado, found in their bibliographies · page 1 of 7 · 1–100; the most cited first."
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---
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# Who cites Greg S. Corrado

9,133 citations of their works from 8,086 papers of our library (of the 140,884 whose bibliographies we read) · 618 with a page here, the most cited first · page 1 of 7 · 1–100 · [← Greg S. Corrado](https://md.research.elamur.ai/free/en/authors/greg-s-corrado/)

**1** [2](https://md.research.elamur.ai/free/en/authors/greg-s-corrado/cited-by/page/2/) … [7](https://md.research.elamur.ai/free/en/authors/greg-s-corrado/cited-by/page/7/) [next →](https://md.research.elamur.ai/free/en/authors/greg-s-corrado/cited-by/page/2/)

1. [**Array Programming with NumPy**Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, et al.*2020 · 23,591 citations*](https://md.research.elamur.ai/free/en/papers/harris-2020-array-programming-numpy/)
2. [**BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding**Jacob Devlin, Ming-Wei Chang, Kenton Lee, et al.*2019 · 23,422 citations*](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/)
3. [**Convolutional Neural Networks for Sentence Classification**Yoon Kim*2014 · 16,218 citations*](https://md.research.elamur.ai/free/en/papers/kim-2014-convolutional-neural-networks-sentence-classification/)
4. [**BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension**Mike Lewis, Yinhan Liu, Naman Goyal, et al.*2020 · 12,752 citations*](https://md.research.elamur.ai/free/en/papers/lewis-2020-bart-denoising-sequence-sequence-pre/)
5. [**Language Models are Few-Shot Learners**Tom B. Brown, Benjamin Mann, Nick Ryder, et al.*2020 · 10,782 citations*](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/)
6. [**TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems**Martín Abadi, Ashish Agarwal, Paul Barham, et al.*2016 · 9,453 citations*](https://md.research.elamur.ai/free/en/papers/abadi-2016-tensorflow-large-scale-machine-learning/)
7. [**Semi-Supervised Classification with Graph Convolutional Networks**Thomas N. Kipf, Max Welling*2016 · 8,809 citations*](https://md.research.elamur.ai/free/en/papers/kipf-2016-semi-supervised-classification-graph-convolutional/)
8. [**Learning Transferable Visual Models From Natural Language Supervision**Alec Radford, Jong Wook Kim, Chris Hallacy, et al.*2021 · 8,295 citations*](https://md.research.elamur.ai/free/en/papers/radford-2021-learning-transferable-visual-models-natural/)
9. [**BioBERT: a pre-trained biomedical language representation model for biomedical text mining**Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, et al.*2019 · 8,281 citations*](https://md.research.elamur.ai/free/en/papers/lee-2019-biobert-pre-trained-biomedical-language/)
10. [**A Simple Framework for Contrastive Learning of Visual Representations**Ting Chen, Simon Kornblith, Mohammad Norouzi, et al.*2020 · 7,774 citations*](https://md.research.elamur.ai/free/en/papers/chen-2020-simple-framework-contrastive-learning-visual/)
11. [**Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer**Colin Raffel, Noam Shazeer, Adam Roberts, et al.*2019 · 7,433 citations*](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/)
12. [**UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction**Leland McInnes, John Healy, James Melville*2018 · 7,136 citations*](https://md.research.elamur.ai/free/en/papers/mcinnes-2018-umap-uniform-manifold-approximation-projection/)
13. [**Representation Learning with Contrastive Predictive Coding**Aaron van den Oord, Yazhe Li, Oriol Vinyals*2018 · 6,826 citations*](https://md.research.elamur.ai/free/en/papers/oord-2018-representation-learning-contrastive-predictive-coding/)
14. [**Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks**Alec Radford, Luke Metz, Soumith Chintala*2015 · 6,519 citations*](https://md.research.elamur.ai/free/en/papers/radford-2015-unsupervised-representation-learning-deep-convolutional/)
15. [**GloVe: Global Vectors for Word Representation**Jeffrey Pennington, Richard Socher, Christopher Manning*2014 · 5,962 citations*](https://md.research.elamur.ai/free/en/papers/pennington-2014-glove-global-vectors-word-representation/)
16. [**Intriguing properties of neural networks**Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, et al.*2013 · 5,870 citations*](https://md.research.elamur.ai/free/en/papers/szegedy-2013-intriguing-properties-neural-networks/)
17. [**Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations**Ranjay Krishna, Yuke Zhu, Oliver Groth, et al.*2016 · 5,747 citations*](https://md.research.elamur.ai/free/en/papers/krishna-2016-visual-genome-connecting-language-vision/)
18. [**Distributed Representations of Sentences and Documents**Quoc V. Le, Tomas Mikolov*2014 · 5,747 citations*](https://md.research.elamur.ai/free/en/papers/le-2014-distributed-representations-sentences-documents/)
19. [**Hierarchical Attention Networks for Document Classification**Zichao Yang, Diyi Yang, Chris Dyer, et al.*2016 · 5,409 citations*](https://md.research.elamur.ai/free/en/papers/yang-2016-hierarchical-attention-networks-document-classification/)
20. [**Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing**Pengfei Liu, Weizhe Yuan, Jinlan Fu, et al.*2021 · 5,375 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-pre-train-prompt-predict-systematic/)
21. [**Character-level Convolutional Networks for Text Classification**Xiang Zhang, Junbo Zhao, Yann LeCun*2015 · 4,753 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2015-character-level-convolutional-networks-text/)
22. [**Billion-scale similarity search with GPUs**Jeff Johnson, Matthijs Douze, Hervé Jégou*2017 · 4,687 citations*](https://md.research.elamur.ai/free/en/papers/johnson-2017-billion-scale-similarity-search-gpus/)
23. [**Inductive Representation Learning on Large Graphs**William L. Hamilton, Rex Ying, Jure Leskovec*2017 · 4,687 citations*](https://md.research.elamur.ai/free/en/papers/hamilton-2017-inductive-representation-learning-large-graphs/)
24. [**Distributed Representations of Words and Phrases and their Compositionality**Tomas Mikolov, Ilya Sutskever, Kai Chen, et al.*2013 · 4,233 citations*](https://md.research.elamur.ai/free/en/papers/mikolov-2013-distributed-representations-words-phrases-their/)
25. [**SimCSE: Simple Contrastive Learning of Sentence Embeddings**Tianyu Gao, Xingcheng Yao, Danqi Chen*2021 · 3,921 citations*](https://md.research.elamur.ai/free/en/papers/gao-2021-simcse-simple-contrastive-learning-sentence/)
26. [**Efficient Estimation of Word Representations in Vector Space**Tomas Mikolov, Kai Chen, Greg Corrado, et al.*2013 · 3,846 citations*](https://md.research.elamur.ai/free/en/papers/mikolov-2013-efficient-estimation-word-representations-vector/)
27. [**Bidirectional LSTM-CRF Models for Sequence Tagging**Zhiheng Huang, Wei Xu, Kai Yu*2015 · 3,802 citations*](https://md.research.elamur.ai/free/en/papers/huang-2015-bidirectional-lstm-crf-models-sequence/)
28. [**A Survey on Evaluation of Large Language Models**Yupeng Chang, Xu Wang, Jindong Wang, et al.*2023 · 3,640 citations*](https://md.research.elamur.ai/free/en/papers/chang-2023-survey-evaluation-large-language-models/)
29. [**Deep Contextualized Word Representations**Matthew E. Peters, Mark Neumann, Mohit Iyyer, et al.*2018 · 3,240 citations*](https://md.research.elamur.ai/free/en/papers/peters-2018-deep-contextualized-word-representations/)
30. [**A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions**Lei Huang, Weijiang Yu, Weitao Ma, et al.*2023 · 3,221 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-hallucination-large-language-models/)
31. [**On the Opportunities and Risks of Foundation Models**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,044 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
32. [**Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books**Yukun Zhu, Ryan Kiros, Richard Zemel, et al.*2015 · 3,030 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2015-aligning-books-movies-towards-story/)
33. [**A Survey of Large Language Models**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,847 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/)
34. [**Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing**Yu Gu; Robert Tinn; Hao Cheng; Michael Lucas; Naoto Usuyama; Xiaodong Liu; Tristan Naumann; Jianfeng Gao;, Hoifung Poon*2020 · 2,636 citations*](https://md.research.elamur.ai/free/en/papers/gu-2020-domain-specific-language-model-pretraining/)
35. [**Attention-based LSTM for Aspect-level Sentiment Classification**Yequan Wang, Minlie Huang, Xiaoyan Zhu, et al.*2016 · 2,609 citations*](https://md.research.elamur.ai/free/en/papers/wang-2016-attention-based-lstm-aspect-level/)
36. [**Evaluating Large Language Models Trained on Code**Mark Chen, Jerry Tworek, Heewoo Jun, et al.*2021 · 2,529 citations*](https://md.research.elamur.ai/free/en/papers/chen-2021-evaluating-large-language-models-trained/)
37. [**Sparks of Artificial General Intelligence: Early experiments with GPT-4**Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, et al.*2023 · 2,389 citations*](https://md.research.elamur.ai/free/en/papers/bubeck-2023-sparks-artificial-general-intelligence-early/)
38. [**A Fast and Accurate Dependency Parser using Neural Networks**Danqi Chen, Christopher Manning*2014 · 2,301 citations*](https://md.research.elamur.ai/free/en/papers/chen-2014-fast-accurate-dependency-parser-neural/)
39. [**Named Entity Recognition with Bidirectional LSTM-CNNs**Jason P. C. Chiu, Eric Nichols*2015 · 2,276 citations*](https://md.research.elamur.ai/free/en/papers/chiu-2015-named-entity-recognition-bidirectional-lstm/)
40. [**GPT-4o System Card**OpenAI, Aaron Hurst, Adam Lerer, et al.*2024 · 2,221 citations*](https://md.research.elamur.ai/free/en/papers/openai-2024-gpt-4o-system-card/)
41. [**Enriching Word Vectors with Subword Information**Piotr Bojanowski, Edouard Grave, Armand Joulin, et al.*2017 · 2,218 citations*](https://md.research.elamur.ai/free/en/papers/bojanowski-2017-enriching-word-vectors-subword-information/)
42. [**Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification**Peng Zhou, Wei Shi, Jun Tian, et al.*2016 · 2,216 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2016-attention-based-bidirectional-long-short/)
43. [**Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings**Tolga Bolukbasi, Kai-Wei Chang, James Zou, et al.*2016 · 2,038 citations*](https://md.research.elamur.ai/free/en/papers/bolukbasi-2016-man-computer-programmer-woman-homemaker/)
44. [**Knowledge Graphs**Aidan Hogan, Eva Blomqvist, Michael Cochez, et al.*2020 · 2,011 citations*](https://md.research.elamur.ai/free/en/papers/hogan-2020-knowledge-graphs/)
45. [**BERT Rediscovers the Classical NLP Pipeline**Ian Tenney, Dipanjan Das, Ellie Pavlick*2019 · 1,993 citations*](https://md.research.elamur.ai/free/en/papers/tenney-2019-bert-rediscovers-classical-nlp-pipeline/)
46. [**Character-Aware Neural Language Models**Yoon Kim, Yacine Jernite, David Sontag, et al.*2015 · 1,852 citations*](https://md.research.elamur.ai/free/en/papers/kim-2015-character-aware-neural-language-models/)
47. [**Unsupervised Data Augmentation for Consistency Training**Qizhe Xie, Zihang Dai, Eduard Hovy, et al.*2019 · 1,837 citations*](https://md.research.elamur.ai/free/en/papers/xie-2019-unsupervised-data-augmentation-consistency-training/)
48. [**AllenNLP: A Deep Semantic Natural Language Processing Platform**Matt Gardner, Joel Grus, Mark Neumann, et al.*2018 · 1,804 citations*](https://md.research.elamur.ai/free/en/papers/gardner-2018-allennlp-deep-semantic-natural-language/)
49. [**A Survey on Hate Speech Detection using Natural Language Processing**Anna Schmidt, Michael Wiegand*2017 · 1,799 citations*](https://md.research.elamur.ai/free/en/papers/schmidt-2017-survey-hate-speech-detection-natural/)
50. [**Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering**Michael Defferrard, Xavier Bresson, Pierre Vandergheynst*2016 · 1,797 citations*](https://md.research.elamur.ai/free/en/papers/defferrard-2016-convolutional-neural-networks-graphs-fast/)
51. [**COMET: A Neural Framework for MT Evaluation**Ricardo Rei, Craig Stewart, Ana C Farinha, et al.*2020 · 1,784 citations*](https://md.research.elamur.ai/free/en/papers/rei-2020-comet-neural-framework-mt-evaluation/)
52. [**A Structured Self-attentive Sentence Embedding**Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, et al.*2017 · 1,773 citations*](https://md.research.elamur.ai/free/en/papers/lin-2017-structured-self-attentive-sentence-embedding/)
53. [**Pre-trained Models for Natural Language Processing: A Survey**Xipeng Qiu, Tianxiang Sun, Yige Xu, et al.*2020 · 1,740 citations*](https://md.research.elamur.ai/free/en/papers/qiu-2020-pre-trained-models-natural-language/)
54. [**Don't count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors**Marco Baroni, Georgiana Dinu, Germán Kruszewski*2014 · 1,678 citations*](https://md.research.elamur.ai/free/en/papers/baroni-2014-don-t-count-predict-systematic/)
55. [**Document Modeling with Gated Recurrent Neural Network for Sentiment Classification**Duyu Tang, Bing Qin, Ting Liu*2015 · 1,675 citations*](https://md.research.elamur.ai/free/en/papers/tang-2015-document-modeling-gated-recurrent-neural/)
56. [**A Deep Reinforced Model for Abstractive Summarization**Romain Paulus, Caiming Xiong, Richard Socher*2017 · 1,626 citations*](https://md.research.elamur.ai/free/en/papers/paulus-2017-deep-reinforced-model-abstractive-summarization/)
57. [**SimLex-999: Evaluating Semantic Models With (Genuine) Similarity Estimation**Felix Hill, Roi Reichart, Anna Korhonen*2015 · 1,622 citations*](https://md.research.elamur.ai/free/en/papers/hill-2015-simlex-999-evaluating-semantic-models/)
58. [**Universal Sentence Encoder**Daniel Cer, Yinfei Yang, Sheng-yi Kong, et al.*2018 · 1,616 citations*](https://md.research.elamur.ai/free/en/papers/cer-2018-universal-sentence-encoder/)
59. [**End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures**Makoto Miwa, Mohit Bansal*2016 · 1,596 citations*](https://md.research.elamur.ai/free/en/papers/miwa-2016-end-end-relation-extraction-lstms/)
60. [**Layer Normalization**Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton*2016 · 1,587 citations*](https://md.research.elamur.ai/free/en/papers/ba-2016-layer-normalization/)
61. [**SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents**Ramesh Nallapati, Feifei Zhai, Bowen Zhou*2016 · 1,566 citations*](https://md.research.elamur.ai/free/en/papers/nallapati-2016-summarunner-recurrent-neural-network-based/)
62. [**Universal Sentence Encoder for English**Daniel Cer, Yinfei Yang, Sheng-yi Kong, et al.*2018 · 1,544 citations*](https://md.research.elamur.ai/free/en/papers/cer-2018-universal-sentence-encoder-english/)
63. [**Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision**Chao Jia, Yinfei Yang, Ye Xia, et al.*2021 · 1,528 citations*](https://md.research.elamur.ai/free/en/papers/jia-2021-scaling-up-visual-vision-language/)
64. [**Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context**Gemini Team, Petko Georgiev, Ving Ian Lei, et al.*2024 · 1,522 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2024-gemini-1-5-unlocking-multimodal/)
65. [**The State and Fate of Linguistic Diversity and Inclusion in the NLP World**Pratik Joshi, Sebastin Santy, Amar Budhiraja, et al.*2020 · 1,492 citations*](https://md.research.elamur.ai/free/en/papers/joshi-2020-state-fate-linguistic-diversity-inclusion/)
66. [**Pre-Training with Whole Word Masking for Chinese BERT**Yiming Cui, Wanxiang Che, Ting Liu, et al.*2019 · 1,485 citations*](https://md.research.elamur.ai/free/en/papers/cui-2019-pre-training-whole-word-masking/)
67. [**Understanding Black-box Predictions via Influence Functions**Pang Wei Koh, Percy Liang*2017 · 1,478 citations*](https://md.research.elamur.ai/free/en/papers/koh-2017-understanding-black-box-predictions-influence/)
68. [**Contextual String Embeddings for Sequence Labeling**Alan Akbik, Duncan Blythe, Roland Vollgraf*2018 · 1,390 citations*](https://md.research.elamur.ai/free/en/papers/akbik-2018-contextual-string-embeddings-sequence-labeling/)
69. [**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/)
70. [**Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification**Duyu Tang, Furu Wei, Nan Yang, et al.*2014 · 1,341 citations*](https://md.research.elamur.ai/free/en/papers/tang-2014-learning-sentiment-specific-word-embedding/)
71. [**Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models**Ryan Kiros, Ruslan Salakhutdinov, Richard S. Zemel*2014 · 1,315 citations*](https://md.research.elamur.ai/free/en/papers/kiros-2014-unifying-visual-semantic-embeddings-multimodal/)
72. [**AraBERT: Transformer-based Model for Arabic Language Understanding**Wissam Antoun, Fady Baly, Hazem Hajj*2020 · 1,297 citations*](https://md.research.elamur.ai/free/en/papers/antoun-2020-arabert-transformer-based-model-arabic/)
73. [**CTRL: A Conditional Transformer Language Model for Controllable Generation**Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, et al.*2019 · 1,273 citations*](https://md.research.elamur.ai/free/en/papers/keskar-2019-ctrl-conditional-transformer-language-model/)
74. [**Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change**William L. Hamilton, Jure Leskovec, Dan Jurafsky*2016 · 1,270 citations*](https://md.research.elamur.ai/free/en/papers/hamilton-2016-diachronic-word-embeddings-reveal-statistical/)
75. [**CamemBERT: a Tasty French Language Model**Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, et al.*2019 · 1,262 citations*](https://md.research.elamur.ai/free/en/papers/martin-2019-camembert-tasty-french-language-model/)
76. [**Latent Retrieval for Weakly Supervised Open Domain Question Answering**Kenton Lee, Ming-Wei Chang, Kristina Toutanova*2019 · 1,234 citations*](https://md.research.elamur.ai/free/en/papers/lee-2019-latent-retrieval-weakly-supervised-open/)
77. [**A Structural Probe for Finding Syntax in Word Representations**John Hewitt, Christopher D. Manning*2019 · 1,228 citations*](https://md.research.elamur.ai/free/en/papers/hewitt-2019-structural-probe-finding-syntax-word/)
78. [**Publicly Available Clinical BERT Embeddings**Emily Alsentzer, John R. Murphy, Willie Boag, et al.*2019 · 1,209 citations*](https://md.research.elamur.ai/free/en/papers/alsentzer-2019-publicly-available-clinical-bert-embeddings/)
79. [**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/)
80. [**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/)
81. [**Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks**Yubo Chen, Liheng Xu, Kang Liu, et al.*2015 · 1,170 citations*](https://md.research.elamur.ai/free/en/papers/chen-2015-event-extraction-dynamic-multi-pooling/)
82. [**"Why Should I Trust You?": Explaining the Predictions of Any Classifier**Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin*2016 · 1,148 citations*](https://md.research.elamur.ai/free/en/papers/ribeiro-2016-why-should-i-trust-explaining/)
83. [**StereoSet: Measuring stereotypical bias in pretrained language models**Moin Nadeem, Anna Bethke, Siva Reddy*2021 · 1,131 citations*](https://md.research.elamur.ai/free/en/papers/nadeem-2021-stereoset-measuring-stereotypical-bias-pretrained/)
84. [**WikiQA: A Challenge Dataset for Open-Domain Question Answering**Yi Yang, Wen-tau Yih, Christopher Meek*2015 · 1,117 citations*](https://md.research.elamur.ai/free/en/papers/yang-2015-wikiqa-challenge-dataset-open-domain/)
85. [**Recurrent Attention Network on Memory for Aspect Sentiment Analysis**Peng Chen, Zhongqian Sun, Lidong Bing, et al.*2017 · 1,113 citations*](https://md.research.elamur.ai/free/en/papers/chen-2017-recurrent-attention-network-memory-aspect/)
86. [**MASS: Masked Sequence to Sequence Pre-training for Language Generation**Kaitao Song, Xu Tan, Tao Qin, et al.*2019 · 1,109 citations*](https://md.research.elamur.ai/free/en/papers/song-2019-mass-masked-sequence-sequence-pre/)
87. [**SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)**Marcos Zampieri, Shervin Malmasi, Preslav Nakov, et al.*2019 · 1,109 citations*](https://md.research.elamur.ai/free/en/papers/zampieri-2019-semeval-2019-task-6-identifying/)
88. [**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/)
89. [**Neural Architectures for Named Entity Recognition**Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, et al.*2016 · 1,021 citations*](https://md.research.elamur.ai/free/en/papers/lample-2016-neural-architectures-named-entity-recognition/)
90. [**Context-Dependent Sentiment Analysis in User-Generated Videos**Soujanya Poria, Erik Cambria, Devamanyu Hazarika, et al.*2017 · 1,019 citations*](https://md.research.elamur.ai/free/en/papers/poria-2017-context-dependent-sentiment-analysis-user/)
91. [**Linguistic Knowledge and Transferability of Contextual Representations**Nelson F. Liu, Matt Gardner, Yonatan Belinkov, et al.*2019 · 1,011 citations*](https://md.research.elamur.ai/free/en/papers/liu-2019-linguistic-knowledge-transferability-contextual-representations/)
92. [**VL-BERT: Pre-training of Generic Visual-Linguistic Representations**Weijie Su, Xizhou Zhu, Yue Cao, et al.*2019 · 1,008 citations*](https://md.research.elamur.ai/free/en/papers/su-2019-vl-bert-pre-training-generic/)
93. [**Neural Summarization by Extracting Sentences and Words**Jianpeng Cheng, Mirella Lapata*2016 · 1,000 citations*](https://md.research.elamur.ai/free/en/papers/cheng-2016-neural-summarization-extracting-sentences-words/)
94. [**On Using Very Large Target Vocabulary for Neural Machine Translation**Sébastien Jean, Kyunghyun Cho, Roland Memisevic, et al.*2015 · 999 citations*](https://md.research.elamur.ai/free/en/papers/jean-2015-very-large-target-vocabulary-neural/)
95. [**Chinese NER Using Lattice LSTM**Yue Zhang, Jie Yang*2018 · 990 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2018-chinese-ner-lattice-lstm/)
96. [**ERNIE 2.0: A Continual Pre-training Framework for Language Understanding**Yu Sun, Shuohuan Wang, Yukun Li, et al.*2019 · 976 citations*](https://md.research.elamur.ai/free/en/papers/sun-2019-ernie-2-0-continual/)
97. [**GPQA: A Graduate-Level Google-Proof Q&A Benchmark**David Rein, Betty Li Hou, Asa Cooper Stickland, et al.*2023 · 964 citations*](https://md.research.elamur.ai/free/en/papers/rein-2023-gpqa-graduate-level-google-proof/)
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