---
title: "Who cites A Neural Probabilistic Language Model"
description: "901 papers of the Elamur Research library cite «A Neural Probabilistic Language Model», found in their bibliographies · page 1 of 2 · 1–100; the most…"
canonical: "https://research.elamur.ai/free/en/papers/bengio-2003-neural-probabilistic-language-model/cited-by/"
---
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# Who cites «A Neural Probabilistic Language Model»

901 papers of our library cite it (of the 140,884 whose bibliographies we read) · 103 with a page here, the most cited first · page 1 of 2 · 1–100 · [← the paper](https://md.research.elamur.ai/free/en/papers/bengio-2003-neural-probabilistic-language-model/)

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1. [**Neural Machine Translation by Jointly Learning to Align and Translate**Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio*2014 · 18,908 citations*](https://md.research.elamur.ai/free/en/papers/bahdanau-2014-neural-machine-translation-jointly-learning/)
2. [**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/)
3. [**Natural Language Processing (almost) from Scratch**Ronan Collobert, Jason Weston, Leon Bottou, et al.*2011 · 6,099 citations*](https://md.research.elamur.ai/free/en/papers/collobert-2011-natural-language-processing-almost-scratch/)
4. [**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/)
5. [**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/)
6. [**Neural Architecture Search with Reinforcement Learning**Barret Zoph, Quoc V. Le*2016 · 4,331 citations*](https://md.research.elamur.ai/free/en/papers/zoph-2016-neural-architecture-search-reinforcement-learning/)
7. [**Learning Word Vectors for Sentiment Analysis**Andrew L. Maas, Raymond E. Daly, Peter T. Pham, et al.*2011 · 4,272 citations*](https://md.research.elamur.ai/free/en/papers/maas-2011-learning-word-vectors-sentiment-analysis/)
8. [**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/)
9. [**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/)
10. [**Linguistic Regularities in Continuous Space Word Representations**Tomas Mikolov, Wen-tau Yih, Geoffrey Zweig*2013 · 3,482 citations*](https://md.research.elamur.ai/free/en/papers/mikolov-2013-linguistic-regularities-continuous-space-word/)
11. [**Language Models as Knowledge Bases?**Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, et al.*2019 · 2,977 citations*](https://md.research.elamur.ai/free/en/papers/petroni-2019-language-models-as-knowledge-bases/)
12. [**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/)
13. [**Word Representations: A Simple and General Method for Semi-Supervised Learning**Joseph Turian, Lev-Arie Ratinov, Yoshua Bengio*2010 · 2,421 citations*](https://md.research.elamur.ai/free/en/papers/turian-2010-word-representations-simple-general-method/)
14. [**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/)
15. [**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/)
16. [**A Neural Conversational Model**Oriol Vinyals, Quoc Le*2015 · 1,759 citations*](https://md.research.elamur.ai/free/en/papers/vinyals-2015-neural-conversational-model/)
17. [**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/)
18. [**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/)
19. [**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/)
20. [**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/)
21. [**Recurrent Continuous Translation Models**Nal Kalchbrenner, Phil Blunsom*2013 · 1,597 citations*](https://md.research.elamur.ai/free/en/papers/kalchbrenner-2013-recurrent-continuous-translation-models/)
22. [**Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions**Richard Socher, Jeffrey Pennington, Eric H. Huang, et al.*2011 · 1,411 citations*](https://md.research.elamur.ai/free/en/papers/socher-2011-semi-supervised-recursive-autoencoders-predicting/)
23. [**Improving Word Representations via Global Context and Multiple Word Prototypes**Eric Huang, Richard Socher, Christopher Manning, et al.*2012 · 1,349 citations*](https://md.research.elamur.ai/free/en/papers/huang-2012-improving-word-representations-global-context/)
24. [**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/)
25. [**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/)
26. [**Language Modeling with Gated Convolutional Networks**Yann N. Dauphin, Angela Fan, Michael Auli, et al.*2016 · 1,305 citations*](https://md.research.elamur.ai/free/en/papers/dauphin-2016-language-modeling-gated-convolutional-networks/)
27. [**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/)
28. [**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/)
29. [**Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification**Li Dong, Furu Wei, Chuanqi Tan, et al.*2014 · 1,211 citations*](https://md.research.elamur.ai/free/en/papers/dong-2014-adaptive-recursive-neural-network-target/)
30. [**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/)
31. [**Very Deep Convolutional Networks for Text Classification**Alexis Conneau, Holger Schwenk, Loïc Barrault, et al.*2017 · 1,110 citations*](https://md.research.elamur.ai/free/en/papers/conneau-2017-very-deep-convolutional-networks-text/)
32. [**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/)
33. [**Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling**Diego Marcheggiani, Ivan Titov*2017 · 1,101 citations*](https://md.research.elamur.ai/free/en/papers/marcheggiani-2017-encoding-sentences-graph-convolutional-networks/)
34. [**Better Word Representations with Recursive Neural Networks for Morphology**Thang Luong, Richard Socher, Christopher Manning*2013 · 1,017 citations*](https://md.research.elamur.ai/free/en/papers/luong-2013-better-word-representations-recursive-neural/)
35. [**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/)
36. [**One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling**Ciprian Chelba, Tomas Mikolov, Mike Schuster, et al.*2013 · 940 citations*](https://md.research.elamur.ai/free/en/papers/chelba-2013-one-billion-word-benchmark-measuring/)
37. [**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/)
38. [**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/)
39. [**Extracting Training Data from Large Language Models**Nicholas Carlini, Florian Tramer, Eric Wallace, et al.*2020 · 901 citations*](https://md.research.elamur.ai/free/en/papers/carlini-2020-extracting-training-data-large-language/)
40. [**Using the Output Embedding to Improve Language Models**Ofir Press, Lior Wolf*2017 · 891 citations*](https://md.research.elamur.ai/free/en/papers/press-2017-output-embedding-improve-language-models/)
41. [**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/)
42. [**Linguistic Regularities in Sparse and Explicit Word Representations**Omer Levy, Yoav Goldberg*2014 · 790 citations*](https://md.research.elamur.ai/free/en/papers/levy-2014-linguistic-regularities-sparse-explicit-word/)
43. [**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/)
44. [**Neural Text Generation from Structured Data with Application to the Biography Domain**Remi Lebret, David Grangier, Michael Auli*2016 · 696 citations*](https://md.research.elamur.ai/free/en/papers/lebret-2016-neural-text-generation-structured-data/)
45. [**Effective LSTMs for Target-Dependent Sentiment Classification**Duyu Tang, Bing Qin, Xiaocheng Feng, et al.*2016 · 693 citations*](https://md.research.elamur.ai/free/en/papers/tang-2016-effective-lstms-target-dependent-sentiment/)
46. [**Bilingual Word Embeddings for Phrase-Based Machine Translation**Will Y. Zou, Richard Socher, Daniel Cer, et al.*2013 · 680 citations*](https://md.research.elamur.ai/free/en/papers/zou-2013-bilingual-word-embeddings-phrase-based/)
47. [**The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks**Nicholas Carlini, Chang Liu, Ulfar Erlingsson, et al.*2018 · 668 citations*](https://md.research.elamur.ai/free/en/papers/carlini-2018-secret-sharer-evaluating-testing-unintended/)
48. [**Fast and Robust Neural Network Joint Models for Statistical Machine Translation**Jacob Devlin, Rabih Zbib, Zhongqiang Huang, et al.*2014 · 666 citations*](https://md.research.elamur.ai/free/en/papers/devlin-2014-fast-robust-neural-network-joint/)
49. [**Curriculum Learning**Yoshua Bengio, Jérôme Louradour, Ronan Collobert, et al.*2009 · 659 citations*](https://md.research.elamur.ai/free/en/papers/bengio-2009-curriculum-learning/)
50. [**Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space**Arvind Neelakantan, Jeevan Shankar, Alexandre Passos, et al.*2014 · 593 citations*](https://md.research.elamur.ai/free/en/papers/neelakantan-2014-efficient-non-parametric-estimation-multiple/)
51. [**Relation Extraction: Perspective from Convolutional Neural Networks**Thien Huu Nguyen, Ralph Grishman*2015 · 590 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2015-relation-extraction-perspective-convolutional-neural/)
52. [**A Hierarchical Bayesian Language Model Based On Pitman-Yor Processes**Yee Whye Teh*2006 · 571 citations*](https://md.research.elamur.ai/free/en/papers/teh-2006-hierarchical-bayesian-language-model-based/)
53. [**Plug and Play Language Models: A Simple Approach to Controlled Text Generation**Sumanth Dathathri, Andrea Madotto, Janice Lan, et al.*2019 · 562 citations*](https://md.research.elamur.ai/free/en/papers/dathathri-2019-plug-play-language-models-simple/)
54. [**Normalized Word Embedding and Orthogonal Transform for Bilingual Word Translation**Chao Xing, Dong Wang, Chao Liu, et al.*2015 · 528 citations*](https://md.research.elamur.ai/free/en/papers/xing-2015-normalized-word-embedding-orthogonal-transform/)
55. [**Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models**Iulian V. Serban, Alessandro Sordoni, Yoshua Bengio, et al.*2015 · 524 citations*](https://md.research.elamur.ai/free/en/papers/serban-2015-building-end-end-dialogue-systems/)
56. [**Inducing Crosslingual Distributed Representations of Words**Alexandre Klementiev, Ivan Titov, Binod Bhattarai*2012 · 501 citations*](https://md.research.elamur.ai/free/en/papers/klementiev-2012-inducing-crosslingual-distributed-representations-words/)
57. [**EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks**Muhammad Abdul-Mageed, Lyle Ungar*2017 · 486 citations*](https://md.research.elamur.ai/free/en/papers/mageed-2017-emonet-fine-grained-emotion-detection/)
58. [**DyNet: The Dynamic Neural Network Toolkit**Graham Neubig, Chris Dyer, Yoav Goldberg, et al.*2017 · 485 citations*](https://md.research.elamur.ai/free/en/papers/neubig-2017-dynet-dynamic-neural-network-toolkit/)
59. [**End-to-end learning of semantic role labeling using recurrent neural networks**Jie Zhou, Wei Xu*2015 · 462 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2015-end-end-learning-semantic-role/)
60. [**Defending Against Neural Fake News**Rowan Zellers, Ari Holtzman, Hannah Rashkin, et al.*2019 · 461 citations*](https://md.research.elamur.ai/free/en/papers/zellers-2019-defending-against-neural-fake-news/)
61. [**Bilingual Word Representations with Monolingual Quality in Mind**Thang Luong, Hieu Pham, Christopher D. Manning*2015 · 453 citations*](https://md.research.elamur.ai/free/en/papers/luong-2015-bilingual-word-representations-monolingual-quality/)
62. [**Experience Grounds Language**Yonatan Bisk, Ari Holtzman, Jesse Thomason, et al.*2020 · 429 citations*](https://md.research.elamur.ai/free/en/papers/bisk-2020-experience-grounds-language/)
63. [**Tailoring Continuous Word Representations for Dependency Parsing**Mohit Bansal, Kevin Gimpel, Karen Livescu*2014 · 418 citations*](https://md.research.elamur.ai/free/en/papers/bansal-2014-tailoring-continuous-word-representations-dependency/)
64. [**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/)
65. [**Generating Sentences by Editing Prototypes**Kelvin Guu, Tatsunori B. Hashimoto, Yonatan Oren, et al.*2017 · 404 citations*](https://md.research.elamur.ai/free/en/papers/guu-2017-generating-sentences-editing-prototypes/)
66. [**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/)
67. [**Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms**Dinghan Shen, Guoyin Wang, Wenlin Wang, et al.*2018 · 383 citations*](https://md.research.elamur.ai/free/en/papers/shen-2018-baseline-needs-more-love-simple/)
68. [**From Paraphrase Database to Compositional Paraphrase Model and Back**John Wieting, Mohit Bansal, Kevin Gimpel, et al.*2015 · 375 citations*](https://md.research.elamur.ai/free/en/papers/wieting-2015-paraphrase-database-compositional-paraphrase-model/)
69. [**Multilingual Models for Compositional Distributed Semantics**Karl Moritz Hermann, Phil Blunsom*2014 · 375 citations*](https://md.research.elamur.ai/free/en/papers/hermann-2014-multilingual-models-compositional-distributed-semantics/)
70. [**SensEmbed: Learning Sense Embeddings for Word and Relational Similarity**Ignacio Iacobacci, Mohammad Taher Pilehvar, Roberto Navigli*2015 · 348 citations*](https://md.research.elamur.ai/free/en/papers/iacobacci-2015-sensembed-learning-sense-embeddings-word/)
71. [**Learning Semantic Hierarchies via Word Embeddings**Ruiji Fu, Jiang Guo, Bing Qin, et al.*2014 · 327 citations*](https://md.research.elamur.ai/free/en/papers/fu-2014-learning-semantic-hierarchies-word-embeddings/)
72. [**Probing Pretrained Language Models for Lexical Semantics**Ivan Vulić, Edoardo Maria Ponti, Robert Litschko, et al.*2020 · 320 citations*](https://md.research.elamur.ai/free/en/papers/vulic-2020-probing-pretrained-language-models-lexical/)
73. [**Representation Learning: A Review and New Perspectives**Yoshua Bengio, Aaron Courville, Pascal Vincent*2012 · 316 citations*](https://md.research.elamur.ai/free/en/papers/bengio-2012-representation-learning-review-new-perspectives/)
74. [**Decoding with Large-Scale Neural Language Models Improves Translation**Ashish Vaswani, Yinggong Zhao, Victoria Fossum, et al.*2013 · 314 citations*](https://md.research.elamur.ai/free/en/papers/vaswani-2013-decoding-large-scale-neural-language/)
75. [**Improving Distributional Similarity with Lessons Learned from Word Embeddings**Omer Levy, Yoav Goldberg, Ido Dagan*2015 · 313 citations*](https://md.research.elamur.ai/free/en/papers/levy-2015-improving-distributional-similarity-lessons-learned/)
76. [**Exploiting Argument Information to Improve Event Detection via Supervised Attention Mechanisms**Shulin Liu, Yubo Chen, Kang Liu, et al.*2017 · 309 citations*](https://md.research.elamur.ai/free/en/papers/liu-2017-exploiting-argument-information-improve-event/)
77. [**A Neural Network Approach to Context-Sensitive Generation of Conversational Responses**Alessandro Sordoni, Michel Galley, Michael Auli, et al.*2015 · 307 citations*](https://md.research.elamur.ai/free/en/papers/sordoni-2015-neural-network-approach-context-sensitive/)
78. [**FlauBERT: Unsupervised Language Model Pre-training for French**Hang Le, Loïc Vial, Jibril Frej, et al.*2019 · 293 citations*](https://md.research.elamur.ai/free/en/papers/le-2019-flaubert-unsupervised-language-model-pre/)
79. [**A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification**Ye Zhang, Byron Wallace*2017 · 254 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2017-sensitivity-analysis-practitioners-guide-convolutional/)
80. [**Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings**Rishi Bommasani, Kelly Davis, Claire Cardie*2020 · 248 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2020-interpreting-pretrained-contextualized-representations-reductions/)
81. [**Logical Natural Language Generation from Open-Domain Tables**Wenhu Chen, Jianshu Chen, Yu Su, et al.*2020 · 209 citations*](https://md.research.elamur.ai/free/en/papers/chen-2020-logical-natural-language-generation-open/)
82. [**Language Modeling for Code-Mixing: The Role of Linguistic Theory based Synthetic Data**Adithya Pratapa, Gayatri Bhat, Monojit Choudhury, et al.*2018 · 202 citations*](https://md.research.elamur.ai/free/en/papers/pratapa-2018-language-modeling-code-mixing-role/)
83. [**Leveraging FrameNet to Improve Automatic Event Detection**Shulin Liu, Yubo Chen, Shizhu He, et al.*2016 · 163 citations*](https://md.research.elamur.ai/free/en/papers/liu-2016-leveraging-framenet-improve-automatic-event/)
84. [**Shallow Convolutional Neural Network for Implicit Discourse Relation Recognition**Biao Zhang, Jinsong Su, Deyi Xiong, et al.*2015 · 161 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2015-shallow-convolutional-neural-network-implicit/)
85. [**Lexicon Infused Phrase Embeddings for Named Entity Resolution**Alexandre Passos, Vineet Kumar, Andrew McCallum*2014 · 143 citations*](https://md.research.elamur.ai/free/en/papers/passos-2014-lexicon-infused-phrase-embeddings-named/)
86. [**Dialogue Act Classification in Domain-Independent Conversations Using a Deep Recurrent Neural Network**Hamed Khanpour, Nishitha Guntakandla, Rodney Nielsen*2016 · 134 citations*](https://md.research.elamur.ai/free/en/papers/khanpour-2016-dialogue-act-classification-domain-independent/)
87. [**Pretraining Language Models with Human Preferences**Tomasz Korbak, Kejian Shi, Angelica Chen, et al.*2023 · 117 citations*](https://md.research.elamur.ai/free/en/papers/korbak-2023-pretraining-language-models-human-preferences/)
88. [**ALLaM: Large Language Models for Arabic and English**M Saiful Bari, Yazeed Alnumay, Norah A. Alzahrani, et al.*2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/bari-2024-allam-large-language-models-arabic/)
89. [**Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP**Sabrina J. Mielke, Zaid Alyafeai, Elizabeth Salesky, et al.*2021 · 89 citations*](https://md.research.elamur.ai/free/en/papers/mielke-2021-between-words-characters-brief-history/)
90. [**A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling**Diego Marcheggiani, Anton Frolov, Ivan Titov*2017 · 86 citations*](https://md.research.elamur.ai/free/en/papers/marcheggiani-2017-simple-accurate-syntax-agnostic-neural/)
91. [**Comparing Word Representations for Implicit Discourse Relation Classification**Chloé Braud, Pascal Denis*2015 · 83 citations*](https://md.research.elamur.ai/free/en/papers/braud-2015-comparing-word-representations-implicit-discourse/)
92. [**Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction**Daniela Gerz, Ivan Vulić, Edoardo Ponti, et al.*2018 · 82 citations*](https://md.research.elamur.ai/free/en/papers/gerz-2018-language-modeling-morphologically-rich-languages/)
93. [**Neural Network Translation Models for Grammatical Error Correction**Shamil Chollampatt, Kaveh Taghipour, Hwee Tou Ng*2016 · 79 citations*](https://md.research.elamur.ai/free/en/papers/chollampatt-2016-neural-network-translation-models-grammatical/)
94. [**A Neural Network Approach to Selectional Preference Acquisition**Van de Cruys, Tim*2014 · 79 citations*](https://md.research.elamur.ai/free/en/papers/cruys-2014-neural-network-approach-selectional-preference/)
95. [**Latent Anaphora Resolution for Cross-Lingual Pronoun Prediction**Christian Hardmeier, Jörg Tiedemann, Joakim Nivre*2013 · 58 citations*](https://md.research.elamur.ai/free/en/papers/hardmeier-2013-latent-anaphora-resolution-cross-lingual/)
96. [**Chinese Zero Pronoun Resolution with Deep Memory Network**Qingyu Yin, Yu Zhang, Weinan Zhang, et al.*2017 · 55 citations*](https://md.research.elamur.ai/free/en/papers/yin-2017-chinese-zero-pronoun-resolution-deep/)
97. [**On the Need of Cross Validation for Discourse Relation Classification**Wei Shi, Vera Demberg*2017 · 25 citations*](https://md.research.elamur.ai/free/en/papers/shi-2017-need-cross-validation-discourse-relation/)
98. [**AraBERT and Farasa Segmentation Based Approach For Sarcasm and Sentiment Detection in Arabic Tweets**Anshul Wadhawan*2021 · 11 citations*](https://md.research.elamur.ai/free/en/papers/wadhawan-2021-arabert-farasa-segmentation-based-approach/)
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