---
title: "Who cites GloVe: Global Vectors for Word Representation"
description: "5,962 papers of the Elamur Research library cite «GloVe: Global Vectors for Word Representation», found in their bibliographies · page 1 of 6 · 1–100; the…"
canonical: "https://research.elamur.ai/free/en/papers/pennington-2014-glove-global-vectors-word-representation/cited-by/"
---
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# Who cites «GloVe: Global Vectors for Word Representation»

5,962 papers of our library cite it (of the 140,884 whose bibliographies we read) · 514 with a page here, the most cited first · page 1 of 6 · 1–100 · [← the paper](https://md.research.elamur.ai/free/en/papers/pennington-2014-glove-global-vectors-word-representation/)

**1** [2](https://md.research.elamur.ai/free/en/papers/pennington-2014-glove-global-vectors-word-representation/cited-by/page/2/) … [6](https://md.research.elamur.ai/free/en/papers/pennington-2014-glove-global-vectors-word-representation/cited-by/page/6/) [next →](https://md.research.elamur.ai/free/en/papers/pennington-2014-glove-global-vectors-word-representation/cited-by/page/2/)

1. [**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/)
2. [**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/)
3. [**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/)
4. [**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/)
5. [**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/)
6. [**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/)
7. [**Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks**Nils Reimers, Iryna Gurevych*2019 · 4,838 citations*](https://md.research.elamur.ai/free/en/papers/reimers-2019-sentence-bert-sentence-embeddings-siamese/)
8. [**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/)
9. [**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/)
10. [**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/)
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13. [**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/)
14. [**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/)
15. [**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/)
16. [**Reading Wikipedia to Answer Open-Domain Questions**Danqi Chen, Adam Fisch, Jason Weston, et al.*2017 · 2,250 citations*](https://md.research.elamur.ai/free/en/papers/chen-2017-reading-wikipedia-answer-open-domain/)
17. [**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/)
18. [**SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems**Alex Wang, Yada Pruksachatkun, Nikita Nangia, et al.*2019 · 2,187 citations*](https://md.research.elamur.ai/free/en/papers/wang-2019-superglue-stickier-benchmark-general-purpose/)
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23. [**GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering**Drew A. Hudson, Christopher D. Manning*2019 · 1,997 citations*](https://md.research.elamur.ai/free/en/papers/hudson-2019-gqa-new-dataset-real-world/)
24. [**Bidirectional Attention Flow for Machine Comprehension**Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, et al.*2016 · 1,956 citations*](https://md.research.elamur.ai/free/en/papers/seo-2016-bidirectional-attention-flow-machine-comprehension/)
25. [**Adversarial Examples for Evaluating Reading Comprehension Systems**Robin Jia, Percy Liang*2017 · 1,935 citations*](https://md.research.elamur.ai/free/en/papers/jia-2017-adversarial-examples-evaluating-reading-comprehension/)
26. [**Personalizing Dialogue Agents: I have a dog, do you have pets too?**Saizheng Zhang, Emily Dinan, Jack Urbanek, et al.*2018 · 1,839 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2018-personalizing-dialogue-agents-i-have/)
27. [**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/)
28. [**What Does BERT Learn about the Structure of Language?**Ganesh Jawahar, Benoît Sagot, Djamé Seddah*2019 · 1,789 citations*](https://md.research.elamur.ai/free/en/papers/jawahar-2019-what-does-bert-learn-about/)
29. [**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/)
30. [**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/)
31. [**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/)
32. [**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/)
33. [**MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations**Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, et al.*2018 · 1,668 citations*](https://md.research.elamur.ai/free/en/papers/poria-2018-meld-multimodal-multi-party-dataset/)
34. [**Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding**Akira Fukui, Dong Huk Park, Daylen Yang, et al.*2016 · 1,642 citations*](https://md.research.elamur.ai/free/en/papers/fukui-2016-multimodal-compact-bilinear-pooling-visual/)
35. [**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/)
36. [**RACE: Large-scale ReAding Comprehension Dataset From Examinations**Guokun Lai, Qizhe Xie, Hanxiao Liu, et al.*2017 · 1,619 citations*](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/)
37. [**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/)
38. [**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/)
39. [**Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph**AmirAli Bagher Zadeh, Paul Pu Liang, Soujanya Poria, et al.*2018 · 1,506 citations*](https://md.research.elamur.ai/free/en/papers/zadeh-2018-multimodal-language-analysis-wild-cmu/)
40. [**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/)
41. [**Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment**Di Jin, Zhijing Jin, Joey Tianyi Zhou, et al.*2019 · 1,318 citations*](https://md.research.elamur.ai/free/en/papers/jin-2019-bert-really-robust-strong-baseline/)
42. [**Long Short-Term Memory-Networks for Machine Reading**Jianpeng Cheng, Li Dong, Mirella Lapata*2016 · 1,300 citations*](https://md.research.elamur.ai/free/en/papers/cheng-2016-long-short-term-memory-networks/)
43. [**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/)
44. [**COMET: Commonsense Transformers for Automatic Knowledge Graph Construction**Antoine Bosselut, Hannah Rashkin, Maarten Sap, et al.*2019 · 1,282 citations*](https://md.research.elamur.ai/free/en/papers/bosselut-2019-comet-commonsense-transformers-automatic-knowledge/)
45. [**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/)
46. [**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/)
47. [**Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems**Tsung-Hsien Wen, Milica Gašić, Nikola Mrkšić, et al.*2015 · 1,192 citations*](https://md.research.elamur.ai/free/en/papers/wen-2015-semantically-conditioned-lstm-based-natural/)
48. [**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/)
49. [**Efficient Low-rank Multimodal Fusion With Modality-Specific Factors**Zhun Liu, Ying Shen, Varun Bharadhwaj Lakshminarasimhan, et al.*2018 · 1,174 citations*](https://md.research.elamur.ai/free/en/papers/liu-2018-efficient-low-rank-multimodal-fusion/)
50. [**The Risk of Racial Bias in Hate Speech Detection**Maarten Sap, Dallas Card, Saadia Gabriel, et al.*2019 · 1,148 citations*](https://md.research.elamur.ai/free/en/papers/sap-2019-risk-racial-bias-hate-speech/)
51. [**CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge**Alon Talmor, Jonathan Herzig, Nicholas Lourie, et al.*2019 · 1,146 citations*](https://md.research.elamur.ai/free/en/papers/talmor-2019-commonsenseqa-question-answering-challenge-targeting/)
52. [**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/)
53. [**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/)
54. [**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/)
55. [**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/)
56. [**Generating Natural Language Adversarial Examples**Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, et al.*2018 · 1,102 citations*](https://md.research.elamur.ai/free/en/papers/alzantot-2018-generating-natural-language-adversarial-examples/)
57. [**SemEval-2016 Task 4: Sentiment Analysis in Twitter**Preslav Nakov, Alan Ritter, Sara Rosenthal, et al.*2016 · 1,081 citations*](https://md.research.elamur.ai/free/en/papers/nakov-2016-semeval-2016-task-4-sentiment/)
58. [**Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets**Yifan Peng, Shankai Yan, Zhiyong Lu*2019 · 1,070 citations*](https://md.research.elamur.ai/free/en/papers/peng-2019-transfer-learning-biomedical-natural-language/)
59. [**Graph Convolution over Pruned Dependency Trees Improves Relation Extraction**Yuhao Zhang, Peng Qi, Christopher D. Manning*2018 · 1,046 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2018-graph-convolution-over-pruned-dependency/)
60. [**The NarrativeQA Reading Comprehension Challenge**Tomáš Kočiský, Jonathan Schwarz, Phil Blunsom, et al.*2018 · 1,026 citations*](https://md.research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/)
61. [**MTEB: Massive Text Embedding Benchmark**Niklas Muennighoff, Nouamane Tazi, Loïc Magne, et al.*2022 · 1,021 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-mteb-massive-text-embedding-benchmark/)
62. [**What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties**Alexis Conneau, German Kruszewski, Guillaume Lample, et al.*2018 · 1,021 citations*](https://md.research.elamur.ai/free/en/papers/conneau-2018-what-you-can-cram-into/)
63. [**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/)
64. [**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/)
65. [**Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders**Tiancheng Zhao, Ran Zhao, Maxine Eskenazi*2017 · 986 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2017-learning-discourse-level-diversity-neural/)
66. [**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/)
67. [**ERNIE: Enhanced Representation through Knowledge Integration**Yu Sun, Shuohuan Wang, Yukun Li, et al.*2019 · 949 citations*](https://md.research.elamur.ai/free/en/papers/sun-2019-ernie-enhanced-representation-through-knowledge/)
68. [**DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs**Dheeru Dua, Yizhong Wang, Pradeep Dasigi, et al.*2019 · 941 citations*](https://md.research.elamur.ai/free/en/papers/dua-2019-drop-reading-comprehension-benchmark-requiring/)
69. [**Patient Knowledge Distillation for BERT Model Compression**Siqi Sun, Yu Cheng, Zhe Gan, et al.*2019 · 939 citations*](https://md.research.elamur.ai/free/en/papers/sun-2019-patient-knowledge-distillation-bert-model/)
70. [**Bottom-Up Abstractive Summarization**Sebastian Gehrmann, Yuntian Deng, Alexander Rush*2018 · 939 citations*](https://md.research.elamur.ai/free/en/papers/gehrmann-2018-bottom-up-abstractive-summarization/)
71. [**Adversarial Example Generation with Syntactically Controlled Paraphrase Networks**Mohit Iyyer, John Wieting, Kevin Gimpel, et al.*2018 · 936 citations*](https://md.research.elamur.ai/free/en/papers/iyyer-2018-adversarial-example-generation-syntactically-controlled/)
72. [**Bilateral Multi-Perspective Matching for Natural Language Sentences**Zhiguo Wang, Wael Hamza, Radu Florian*2017 · 923 citations*](https://md.research.elamur.ai/free/en/papers/wang-2017-bilateral-multi-perspective-matching-natural/)
73. [**Deep Unordered Composition Rivals Syntactic Methods for Text Classification**Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, et al.*2015 · 914 citations*](https://md.research.elamur.ai/free/en/papers/iyyer-2015-deep-unordered-composition-rivals-syntactic/)
74. [**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/)
75. [**SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation**Daniel Cer, Mona Diab, Eneko Agirre, et al.*2017 · 907 citations*](https://md.research.elamur.ai/free/en/papers/cer-2017-semeval-2017-task-1-semantic/)
76. [**CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models**Nikita Nangia, Clara Vania, Rasika Bhalerao, et al.*2020 · 886 citations*](https://md.research.elamur.ai/free/en/papers/nangia-2020-crows-pairs-challenge-dataset-measuring/)
77. [**Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency**Shuhuai Ren, Yihe Deng, Kun He, et al.*2019 · 884 citations*](https://md.research.elamur.ai/free/en/papers/ren-2019-generating-natural-language-adversarial-examples/)
78. [**End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF**Xuezhe Ma, Eduard Hovy*2016 · 852 citations*](https://md.research.elamur.ai/free/en/papers/ma-2016-end-end-sequence-labeling-bi/)
79. [**DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation**Deepanway Ghosal, Navonil Majumder, Soujanya Poria, et al.*2019 · 850 citations*](https://md.research.elamur.ai/free/en/papers/ghosal-2019-dialoguegcn-graph-convolutional-neural-network/)
80. [**BERT-ATTACK: Adversarial Attack Against BERT Using BERT**Linyang Li, Ruotian Ma, Qipeng Guo, et al.*2020 · 840 citations*](https://md.research.elamur.ai/free/en/papers/li-2020-bert-attack-adversarial-attack-against/)
81. [**Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers**Ji Gao, Jack Lanchantin, Mary Lou Soffa, et al.*2018 · 836 citations*](https://md.research.elamur.ai/free/en/papers/gao-2018-black-box-generation-adversarial-text/)
82. [**On the Sentence Embeddings from Pre-trained Language Models**Bohan Li, Hao Zhou, Junxian He, et al.*2020 · 830 citations*](https://md.research.elamur.ai/free/en/papers/li-2020-sentence-embeddings-pre-trained-language/)
83. [**Gated Self-Matching Networks for Reading Comprehension and Question Answering**Wenhui Wang, Nan Yang, Furu Wei, et al.*2017 · 823 citations*](https://md.research.elamur.ai/free/en/papers/wang-2017-gated-self-matching-networks-reading/)
84. [**DocVQA: A Dataset for VQA on Document Images**Minesh Mathew, Dimosthenis Karatzas, C.V. Jawahar*2020 · 820 citations*](https://md.research.elamur.ai/free/en/papers/mathew-2020-docvqa-dataset-vqa-document-images/)
85. [**ERASER: A Benchmark to Evaluate Rationalized NLP Models**Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, et al.*2020 · 820 citations*](https://md.research.elamur.ai/free/en/papers/deyoung-2020-eraser-benchmark-evaluate-rationalized-nlp/)
86. [**SemEval-2016 Task 1: Semantic Textual Similarity, Monolingual and Cross-Lingual Evaluation**Eneko Agirre, Carmen Banea, Daniel Cer, et al.*2016 · 820 citations*](https://md.research.elamur.ai/free/en/papers/agirre-2016-semeval-2016-task-1-semantic/)
87. [**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/)
88. [**ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission**Kexin Huang, Jaan Altosaar, Rajesh Ranganath*2019 · 788 citations*](https://md.research.elamur.ai/free/en/papers/huang-2019-clinicalbert-modeling-clinical-notes-predicting/)
89. [**Semi-supervised sequence tagging with bidirectional language models**Matthew E. Peters, Waleed Ammar, Chandra Bhagavatula, et al.*2017 · 786 citations*](https://md.research.elamur.ai/free/en/papers/peters-2017-semi-supervised-sequence-tagging-bidirectional/)
90. [**Relational Graph Attention Network for Aspect-based Sentiment Analysis**Kai Wang, Weizhou Shen, Yunyi Yang, et al.*2020 · 775 citations*](https://md.research.elamur.ai/free/en/papers/wang-2020-relational-graph-attention-network-aspect/)
91. [**Ask Me Anything: Dynamic Memory Networks for Natural Language Processing**Ankit Kumar, Ozan Irsoy, Peter Ondruska, et al.*2015 · 775 citations*](https://md.research.elamur.ai/free/en/papers/kumar-2015-ask-me-anything-dynamic-memory/)
92. [**On Measuring Social Biases in Sentence Encoders**Chandler May, Alex Wang, Shikha Bordia, et al.*2019 · 761 citations*](https://md.research.elamur.ai/free/en/papers/may-2019-measuring-social-biases-sentence-encoders/)
93. [**EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks**Jason Wei, Kai Zou*2019 · 750 citations*](https://md.research.elamur.ai/free/en/papers/wei-2019-eda-easy-data-augmentation-techniques/)
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