---
title: "Who cites Recursive Deep Models for — 2,591 citing papers"
description: "2,591 papers of the Elamur Research library cite «Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank», found…"
canonical: "https://research.elamur.ai/free/en/papers/socher-2013-recursive-deep-models-semantic-compositionality/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/socher-2013-recursive-deep-models-semantic-compositionality/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Recursive Deep Models for Semantic…](https://md.research.elamur.ai/free/en/papers/socher-2013-recursive-deep-models-semantic-compositionality/) / cited by

# Who cites «Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank»

2,591 papers of our library cite it (of the 149,439 whose bibliographies we read) · 364 with a page here, the most cited first · page 1 of 4 · 1–100 · [← back to «Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank»](https://md.research.elamur.ai/free/en/papers/socher-2013-recursive-deep-models-semantic-compositionality/)

**1** [2](https://md.research.elamur.ai/free/en/papers/socher-2013-recursive-deep-models-semantic-compositionality/cited-by/page/2/) [3](https://md.research.elamur.ai/free/en/papers/socher-2013-recursive-deep-models-semantic-compositionality/cited-by/page/3/) [4](https://md.research.elamur.ai/free/en/papers/socher-2013-recursive-deep-models-semantic-compositionality/cited-by/page/4/) [next →](https://md.research.elamur.ai/free/en/papers/socher-2013-recursive-deep-models-semantic-compositionality/cited-by/page/2/)

1. **[BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Jacob Devlin, Ming-Wei Chang, Kenton Lee, et al.*2019 · 56,209 citations*
2. **[Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://md.research.elamur.ai/free/en/papers/reimers-2019-sentence-bert-sentence-embeddings-siamese/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Nils Reimers, Iryna Gurevych*2019 · 17,096 citations*
3. **[Convolutional Neural Networks for Sentence Classification](https://md.research.elamur.ai/free/en/papers/kim-2014-convolutional-neural-networks-sentence-classification/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yoon Kim*2014 · 16,299 citations*
4. **[Deep Contextualized Word Representations](https://md.research.elamur.ai/free/en/papers/peters-2018-deep-contextualized-word-representations/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Matthew E. Peters, Mark Neumann, Mohit Iyyer, et al.*2018 · 14,944 citations*
5. **[BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://md.research.elamur.ai/free/en/papers/lewis-2020-bart-denoising-sequence-sequence-pre/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mike Lewis, Yinhan Liu, Naman Goyal, et al.*2020 · 12,856 citations*
6. [**Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer arXiv:1910.10683**Colin Raffel, Noam Shazeer, Adam Roberts, et al.*2019 · 11,325 citations*](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/)
7. [**Training language models to follow instructions with human feedback arXiv:2203.02155**Long Ouyang, Jeff Wu, Xu Jiang, et al.*2022 · 10,224 citations*](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/)
8. [**RoBERTa: A Robustly Optimized BERT Pretraining Approach arXiv:1907.11692**Yinhan Liu, Myle Ott, Naman Goyal, et al.*2019 · 9,837 citations*](https://md.research.elamur.ai/free/en/papers/liu-2019-roberta-robustly-optimized-bert-pretraining/)
9. [**Learning Transferable Visual Models From Natural Language Supervision arXiv:2103.00020**Alec Radford, Jong Wook Kim, Chris Hallacy, et al.*2021 · 8,767 citations*](https://md.research.elamur.ai/free/en/papers/radford-2021-learning-transferable-visual-models-natural/)
10. [**LoRA: Low-Rank Adaptation of Large Language Models arXiv:2106.09685**Edward J. Hu, Yelong Shen, Phillip Wallis, et al.*2021 · 8,661 citations*](https://md.research.elamur.ai/free/en/papers/hu-2021-lora-low-rank-adaptation-large/)
11. **[The Stanford CoreNLP Natural Language Processing Toolkit](https://md.research.elamur.ai/free/en/papers/manning-2014-stanford-corenlp-natural-language-processing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Christopher Manning, Mihai Surdeanu, John Bauer, et al.*2014 · 8,644 citations*
12. **[GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/wang-2018-glue-multi-task-benchmark-analysis/) [EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP](https://md.research.elamur.ai/free/en/venues/emnlp-workshop-blackboxnlp-analyzing-and-interpreting-neural-networks-for-nlp/)**Alex Wang, Amanpreet Singh, Julian Michael, et al.*2018 · 7,276 citations*
13. [**Distributed Representations of Sentences and Documents arXiv:1405.4053**Quoc V. Le, Tomas Mikolov*2014 · 5,803 citations*](https://md.research.elamur.ai/free/en/papers/le-2014-distributed-representations-sentences-documents/)
14. **[Hierarchical Attention Networks for Document Classification](https://md.research.elamur.ai/free/en/papers/yang-2016-hierarchical-attention-networks-document-classification/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Zichao Yang, Diyi Yang, Chris Dyer, et al.*2016 · 5,436 citations*
15. **[Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/papers/liu-2021-pre-train-prompt-predict-systematic/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Pengfei Liu, Weizhe Yuan, Jinlan Fu, et al.*2021 · 5,423 citations*
16. **[A large annotated corpus for learning natural language inference](https://md.research.elamur.ai/free/en/papers/bowman-2015-large-annotated-corpus-learning-natural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Samuel R. Bowman, Gabor Angeli, Christopher Potts, et al.*2015 · 5,299 citations*
17. **[SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://md.research.elamur.ai/free/en/papers/gao-2021-simcse-simple-contrastive-learning-sentence/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tianyu Gao, Xingcheng Yao, Danqi Chen*2021 · 3,942 citations*
18. [**RoFormer: Enhanced Transformer with Rotary Position Embedding arXiv:2104.09864**Jianlin Su, Yu Lu, Shengfeng Pan, et al.*2021 · 3,028 citations*](https://md.research.elamur.ai/free/en/papers/su-2021-roformer-enhanced-transformer-rotary-position/)
19. [**ALBERT: A Lite BERT for Self-supervised Learning of Language Representations arXiv:1909.11942**Zhenzhong Lan, Mingda Chen, Sebastian Goodman, et al.*2019 · 2,806 citations*](https://md.research.elamur.ai/free/en/papers/lan-2019-albert-lite-bert-self-supervised/)
20. **[Attention-based LSTM for Aspect-level Sentiment Classification](https://md.research.elamur.ai/free/en/papers/wang-2016-attention-based-lstm-aspect-level/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yequan Wang, Minlie Huang, Xiaoyan Zhu, et al.*2016 · 2,621 citations*
21. **[SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://md.research.elamur.ai/free/en/papers/joshi-2019-spanbert-improving-pre-training-representing/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Mandar Joshi, Danqi Chen, Yinhan Liu, et al.*2019 · 2,616 citations*
22. **[TinyBERT: Distilling BERT for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/jiao-2020-tinybert-distilling-bert-natural-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiaoqi Jiao, Yichun Yin, Lifeng Shang, et al.*2020 · 2,378 citations*
23. [**Relational inductive biases, deep learning, and graph networks arXiv:1806.01261**Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, et al.*2018 · 2,335 citations*](https://md.research.elamur.ai/free/en/papers/battaglia-2018-relational-inductive-biases-deep-learning/)
24. [**SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems arXiv:1905.00537**Alex Wang, Yada Pruksachatkun, Nikita Nangia, et al.*2019 · 2,227 citations*](https://md.research.elamur.ai/free/en/papers/wang-2019-superglue-stickier-benchmark-general-purpose/)
25. **[Tensor Fusion Network for Multimodal Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/zadeh-2017-tensor-fusion-network-multimodal-sentiment/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Amir Zadeh, Minghai Chen, Soujanya Poria, et al.*2017 · 2,125 citations*
26. **[Making Pre-trained Language Models Better Few-shot Learners](https://md.research.elamur.ai/free/en/papers/gao-2021-making-pre-trained-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianyu Gao, Adam Fisch, Danqi Chen*2021 · 1,987 citations*
27. [**Finetuned Language Models Are Zero-Shot Learners arXiv:2109.01652**Jason Wei, Maarten Bosma, Vincent Y. Zhao, et al.*2021 · 1,954 citations*](https://md.research.elamur.ai/free/en/papers/wei-2021-finetuned-language-models-zero-shot/)
28. **[AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts](https://md.research.elamur.ai/free/en/papers/shin-2020-autoprompt-eliciting-knowledge-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, et al.*2020 · 1,909 citations*
29. [**DeBERTa: Decoding-enhanced BERT with Disentangled Attention arXiv:2006.03654**Pengcheng He, Xiaodong Liu, Jianfeng Gao, et al.*2020 · 1,828 citations*](https://md.research.elamur.ai/free/en/papers/he-2020-deberta-decoding-enhanced-bert-disentangled/)
30. **[Reasoning With Neural Tensor Networks for Knowledge Base Completion](https://md.research.elamur.ai/free/en/papers/socher-2013-reasoning-neural-tensor-networks-knowledge/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Richard Socher, Danqi Chen, Christopher D. Manning, et al.*2013 · 1,810 citations*
31. [**A Structured Self-attentive Sentence Embedding arXiv:1703.03130**Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, et al.*2017 · 1,797 citations*](https://md.research.elamur.ai/free/en/papers/lin-2017-structured-self-attentive-sentence-embedding/)
32. [**Pre-trained Models for Natural Language Processing: A Survey arXiv:2003.08271 · SCIENCE CHINA Technological Sciences,2020, 63, 1872-1897**Xipeng Qiu, Tianxiang Sun, Yige Xu, et al.*2020 · 1,759 citations*](https://md.research.elamur.ai/free/en/papers/qiu-2020-pre-trained-models-natural-language/)
33. [**Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj**Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.*2022 · 1,756 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/)
34. **[Document Modeling with Gated Recurrent Neural Network for Sentiment Classification](https://md.research.elamur.ai/free/en/papers/tang-2015-document-modeling-gated-recurrent-neural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Duyu Tang, Bing Qin, Ting Liu*2015 · 1,686 citations*
35. [**Retrieval-Augmented Generation for Large Language Models: A Survey arXiv:2312.10997**Yunfan Gao, Yun Xiong, Xinyu Gao, et al.*2023 · 1,663 citations*](https://md.research.elamur.ai/free/en/papers/gao-2023-retrieval-augmented-generation-large-language/)
36. **[SemEval-2016 Task 5: Aspect Based Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/pontiki-2016-semeval-2016-task-5-aspect/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, et al.*2016 · 1,661 citations*
37. [**Universal Sentence Encoder arXiv:1803.11175**Daniel Cer, Yinfei Yang, Sheng-yi Kong, et al.*2018 · 1,629 citations*](https://md.research.elamur.ai/free/en/papers/cer-2018-universal-sentence-encoder/)
38. **[Universal Sentence Encoder for English](https://md.research.elamur.ai/free/en/papers/cer-2018-universal-sentence-encoder-english/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Daniel Cer, Yinfei Yang, Sheng-yi Kong, et al.*2018 · 1,546 citations*
39. **[Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph](https://md.research.elamur.ai/free/en/papers/zadeh-2018-multimodal-language-analysis-wild-cmu/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**AmirAli Bagher Zadeh, Paul Pu Liang, Soujanya Poria, et al.*2018 · 1,522 citations*
40. **[GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://md.research.elamur.ai/free/en/papers/du-2022-glm-general-language-model-pretraining/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhengxiao Du, Yujie Qian, Xiao Liu, et al.*2022 · 1,455 citations*
41. [**Unified Language Model Pre-training for Natural Language Understanding and Generation arXiv:1905.03197**Li Dong, Nan Yang, Wenhui Wang, et al.*2019 · 1,422 citations*](https://md.research.elamur.ai/free/en/papers/dong-2019-unified-language-model-pre-training/)
42. **[Language-agnostic BERT Sentence Embedding](https://md.research.elamur.ai/free/en/papers/feng-2020-language-agnostic-bert-sentence-embedding/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, et al.*2020 · 1,413 citations*
43. **[Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification](https://md.research.elamur.ai/free/en/papers/tang-2014-learning-sentiment-specific-word-embedding/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Duyu Tang, Furu Wei, Nan Yang, et al.*2014 · 1,346 citations*
44. [**DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing arXiv:2111.09543**Pengcheng He, Jianfeng Gao, Weizhu Chen*2021 · 1,320 citations*](https://md.research.elamur.ai/free/en/papers/he-2021-debertav3-improving-deberta-using-electra/)
45. **[Long Short-Term Memory-Networks for Machine Reading](https://md.research.elamur.ai/free/en/papers/cheng-2016-long-short-term-memory-networks/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jianpeng Cheng, Li Dong, Mirella Lapata*2016 · 1,313 citations*
46. [**What Makes Good In-Context Examples for GPT-3? Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures**Jiachang Liu, Dinghan Shen, Yizhe Zhang, et al.*2022 · 1,256 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-what-makes-good-context-examples/)
47. **[Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification](https://md.research.elamur.ai/free/en/papers/dong-2014-adaptive-recursive-neural-network-target/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Li Dong, Furu Wei, Chuanqi Tan, et al.*2014 · 1,217 citations*
48. **[Attention is not not Explanation](https://md.research.elamur.ai/free/en/papers/wiegreffe-2019-attention-is-not-not-explanation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sarah Wiegreffe, Yuval Pinter*2019 · 1,208 citations*
49. **[Aspect Level Sentiment Classification with Deep Memory Network](https://md.research.elamur.ai/free/en/papers/tang-2016-aspect-level-sentiment-classification-deep/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Duyu Tang, Bing Qin, Ting Liu*2016 · 1,197 citations*
50. [**MASS: Masked Sequence to Sequence Pre-training for Language Generation arXiv:1905.02450**Kaitao Song, Xu Tan, Tao Qin, et al.*2019 · 1,143 citations*](https://md.research.elamur.ai/free/en/papers/song-2019-mass-masked-sequence-sequence-pre/)
51. **[Recurrent Attention Network on Memory for Aspect Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/chen-2017-recurrent-attention-network-memory-aspect/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Peng Chen, Zhongqian Sun, Lidong Bing, et al.*2017 · 1,119 citations*
52. **[Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling](https://md.research.elamur.ai/free/en/papers/marcheggiani-2017-encoding-sentences-graph-convolutional-networks/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Diego Marcheggiani, Ivan Titov*2017 · 1,106 citations*
53. **[Quantifying Attention Flow in Transformers](https://md.research.elamur.ai/free/en/papers/abnar-2020-quantifying-attention-flow-transformers/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Samira Abnar, Willem Zuidema*2020 · 1,064 citations*
54. **[Context-Dependent Sentiment Analysis in User-Generated Videos](https://md.research.elamur.ai/free/en/papers/poria-2017-context-dependent-sentiment-analysis-user/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Soujanya Poria, Erik Cambria, Devamanyu Hazarika, et al.*2017 · 1,029 citations*
55. **[Attention is not Explanation](https://md.research.elamur.ai/free/en/papers/jain-2019-attention-is-not-explanation/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Sarthak Jain, Byron C. Wallace*2019 · 1,021 citations*
56. **[MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://md.research.elamur.ai/free/en/papers/sun-2020-mobilebert-compact-task-agnostic-bert/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiqing Sun, Hongkun Yu, Xiaodan Song, et al.*2020 · 1,016 citations*
57. **[ERNIE 2.0: A Continual Pre-training Framework for Language Understanding](https://md.research.elamur.ai/free/en/papers/sun-2019-ernie-2-0-continual/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Yu Sun, Shuohuan Wang, Yukun Li, et al.*2019 · 995 citations*
58. [**A Survey on In-context Learning arXiv:2301.00234**Qingxiu Dong, Lei Li, Damai Dai, et al.*2023 · 978 citations*](https://md.research.elamur.ai/free/en/papers/dong-2023-survey-context-learning/)
59. [**AdapterFusion: Non-Destructive Task Composition for Transfer Learning arXiv:2005.00247 · Proceedings of EACL 2021**Jonas Pfeiffer, Aishwarya Kamath, Andreas Rücklé, et al.*2020 · 958 citations*](https://md.research.elamur.ai/free/en/papers/pfeiffer-2020-adapterfusion-non-destructive-task-composition/)
60. [**MPNet: Masked and Permuted Pre-training for Language Understanding arXiv:2004.09297**Kaitao Song, Xu Tan, Tao Qin, et al.*2020 · 957 citations*](https://md.research.elamur.ai/free/en/papers/song-2020-mpnet-masked-permuted-pre-training/)
61. [**Patient Knowledge Distillation for BERT Model Compression arXiv:1908.09355**Siqi Sun, Yu Cheng, Zhe Gan, et al.*2019 · 947 citations*](https://md.research.elamur.ai/free/en/papers/sun-2019-patient-knowledge-distillation-bert-model/)
62. **[Adversarial Example Generation with Syntactically Controlled Paraphrase Networks](https://md.research.elamur.ai/free/en/papers/iyyer-2018-adversarial-example-generation-syntactically-controlled/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Mohit Iyyer, John Wieting, Kevin Gimpel, et al.*2018 · 938 citations*
63. **[Deep Unordered Composition Rivals Syntactic Methods for Text Classification](https://md.research.elamur.ai/free/en/papers/iyyer-2015-deep-unordered-composition-rivals-syntactic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, et al.*2015 · 926 citations*
64. [**Transition-Based Dependency Parsing with Stack Long Short-Term Memory**Chris Dyer, Miguel Ballesteros, Wang Ling, et al.*2015 · 868 citations*](https://md.research.elamur.ai/free/en/papers/dyer-2015-transition-based-dependency-parsing-stack/)
65. [**A Convolutional Neural Network for Modelling Sentences arXiv:1404.2188**Nal Kalchbrenner, Edward Grefenstette, Phil Blunsom*2014 · 859 citations*](https://md.research.elamur.ai/free/en/papers/kalchbrenner-2014-convolutional-neural-network-modelling-sentences/)
66. **[Visualizing and Understanding Neural Models in NLP](https://md.research.elamur.ai/free/en/papers/li-2016-visualizing-understanding-neural-models-nlp/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Jiwei Li, Xinlei Chen, Eduard Hovy, et al.*2016 · 855 citations*
67. **[DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding](https://md.research.elamur.ai/free/en/papers/shen-2017-disan-directional-self-attention-network/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Tao Shen, Tianyi Zhou, Guodong Long, et al.*2017 · 854 citations*
68. [**Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers arXiv:1801.04354**Ji Gao, Jack Lanchantin, Mary Lou Soffa, et al.*2018 · 840 citations*](https://md.research.elamur.ai/free/en/papers/gao-2018-black-box-generation-adversarial-text/)
69. **[Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://md.research.elamur.ai/free/en/papers/qin-2023-chatgpt-general-purpose-natural-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Chengwei Qin, Aston Zhang, Zhuosheng Zhang, et al.*2023 · 828 citations*
70. [**Calibrate Before Use: Improving Few-Shot Performance of Language Models arXiv:2102.09690**Tony Z. Zhao, Eric Wallace, Shi Feng, et al.*2021 · 825 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2021-calibrate-before-use-improving-few/)
71. **[Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://md.research.elamur.ai/free/en/papers/yang-2023-harnessing-power-llms-practice-survey/) [ACM Transactions on Knowledge Discovery from Data](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-knowledge-discovery-from-data/)**Jingfeng Yang, Hongye Jin, Ruixiang Tang, et al.*2023 · 814 citations*
72. **[Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations](https://md.research.elamur.ai/free/en/papers/kobayashi-2018-contextual-augmentation-data-augmentation-words/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Sosuke Kobayashi*2018 · 810 citations*
73. [**Ask Me Anything: Dynamic Memory Networks for Natural Language Processing arXiv:1506.07285**Ankit Kumar, Ozan Irsoy, Peter Ondruska, et al.*2015 · 801 citations*](https://md.research.elamur.ai/free/en/papers/kumar-2015-ask-me-anything-dynamic-memory/)
74. **[AdapterHub: A Framework for Adapting Transformers](https://md.research.elamur.ai/free/en/papers/pfeiffer-2020-adapterhub-framework-adapting-transformers/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jonas Pfeiffer, Andreas Rücklé, Clifton Poth, et al.*2020 · 754 citations*
75. [**Revealing the Dark Secrets of BERT arXiv:1908.08593**Olga Kovaleva, Alexey Romanov, Anna Rogers, et al.*2019 · 750 citations*](https://md.research.elamur.ai/free/en/papers/kovaleva-2019-revealing-dark-secrets-bert/)
76. **[SentEval: An Evaluation Toolkit for Universal Sentence Representations](https://md.research.elamur.ai/free/en/papers/conneau-2018-senteval-evaluation-toolkit-universal-sentence/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Alexis Conneau, Douwe Kiela*2018 · 747 citations*
77. **[Adversarial Multi-task Learning for Text Classification](https://md.research.elamur.ai/free/en/papers/liu-2017-adversarial-multi-task-learning-text/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Pengfei Liu, Xipeng Qiu, Xuanjing Huang*2017 · 729 citations*
78. **[Rumor Detection on Twitter with Tree-structured Recursive Neural Networks](https://md.research.elamur.ai/free/en/papers/ma-2018-rumor-detection-twitter-tree-structured/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jing Ma, Wei Gao, Kam-Fai Wong*2018 · 709 citations*
79. **[Effective LSTMs for Target-Dependent Sentiment Classification](https://md.research.elamur.ai/free/en/papers/tang-2016-effective-lstms-target-dependent-sentiment/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Duyu Tang, Bing Qin, Xiaocheng Feng, et al.*2016 · 697 citations*
80. [**Well-Read Students Learn Better: On the Importance of Pre-training Compact Models arXiv:1908.08962**Iulia Turc, Ming-Wei Chang, Kenton Lee, et al.*2019 · 678 citations*](https://md.research.elamur.ai/free/en/papers/turc-2019-well-read-students-learn-better/)
81. **[Harnessing Deep Neural Networks with Logic Rules](https://md.research.elamur.ai/free/en/papers/hu-2016-harnessing-deep-neural-networks-logic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiting Hu, Xuezhe Ma, Zhengzhong Liu, et al.*2016 · 627 citations*
82. [**Towards a Unified View of Parameter-Efficient Transfer Learning arXiv:2110.04366**Junxian He, Chunting Zhou, Xuezhe Ma, et al.*2021 · 619 citations*](https://md.research.elamur.ai/free/en/papers/he-2021-towards-unified-view-parameter-efficient/)
83. **[Parsing Argumentation Structures in Persuasive Essays](https://md.research.elamur.ai/free/en/papers/stab-2017-parsing-argumentation-structures-persuasive-essays/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Christian Stab, Iryna Gurevych*2017 · 613 citations*
84. [**Text Embeddings by Weakly-Supervised Contrastive Pre-training arXiv:2212.03533**Liang Wang, Nan Yang, Xiaolong Huang, et al.*2022 · 598 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-text-embeddings-weakly-supervised-contrastive/)
85. **[Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings](https://md.research.elamur.ai/free/en/papers/liu-2015-fine-grained-opinion-mining-recurrent/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Pengfei Liu, Shafiq Joty, Helen Meng*2015 · 593 citations*
86. **[Relation Extraction: Perspective from Convolutional Neural Networks](https://md.research.elamur.ai/free/en/papers/nguyen-2015-relation-extraction-perspective-convolutional-neural/) [Workshop on Vector Space Modeling for Natural Language Processing](https://md.research.elamur.ai/free/en/venues/workshop-on-vector-space-modeling-for-natural-language-processing/)**Thien Huu Nguyen, Ralph Grishman*2015 · 592 citations*
87. [**Plug and Play Language Models: A Simple Approach to Controlled Text Generation arXiv:1912.02164**Sumanth Dathathri, Andrea Madotto, Janice Lan, et al.*2019 · 578 citations*](https://md.research.elamur.ai/free/en/papers/dathathri-2019-plug-play-language-models-simple/)
88. [**An efficient framework for learning sentence representations arXiv:1803.02893**Lajanugen Logeswaran, Honglak Lee*2018 · 577 citations*](https://md.research.elamur.ai/free/en/papers/logeswaran-2018-efficient-framework-learning-sentence-representations/)
89. [**Reducing Transformer Depth on Demand with Structured Dropout arXiv:1909.11556**Angela Fan, Edouard Grave, Armand Joulin*2019 · 560 citations*](https://md.research.elamur.ai/free/en/papers/fan-2019-reducing-transformer-depth-demand-structured/)
90. [**The Natural Language Decathlon: Multitask Learning as Question Answering arXiv:1806.08730**Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, et al.*2018 · 545 citations*](https://md.research.elamur.ai/free/en/papers/mccann-2018-natural-language-decathlon-multitask-learning/)
91. **[Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning](https://md.research.elamur.ai/free/en/papers/aghajanyan-2021-intrinsic-dimensionality-explains-effectiveness-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Armen Aghajanyan, Sonal Gupta, Luke Zettlemoyer*2021 · 542 citations*
92. [**Toward Controlled Generation of Text arXiv:1703.00955**Zhiting Hu, Zichao Yang, Xiaodan Liang, et al.*2017 · 537 citations*](https://md.research.elamur.ai/free/en/papers/hu-2017-toward-controlled-generation-text/)
93. [**Unleashing the potential of prompt engineering for large language models arXiv:2310.14735 · Patterns 6(6) 101,260 (2025)**Banghao Chen, Zhaofeng Zhang, Nicolas Langrene, et al.*2023 · 536 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-unleashing-potential-prompt-engineering-large/)
94. **[Stance Detection with Bidirectional Conditional Encoding](https://md.research.elamur.ai/free/en/papers/augenstein-2016-stance-detection-bidirectional-conditional-encoding/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Isabelle Augenstein, Tim Rocktäschel, Andreas Vlachos, et al.*2016 · 522 citations*
95. **[DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations](https://md.research.elamur.ai/free/en/papers/giorgi-2021-declutr-deep-contrastive-learning-unsupervised/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**John Giorgi, Osvald Nitski, Bo Wang, et al.*2021 · 516 citations*
96. **[Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/wang-2016-recursive-neural-conditional-random-fields/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, et al.*2016 · 516 citations*
97. [**Learned in Translation: Contextualized Word Vectors arXiv:1708.00107**Bryan McCann, James Bradbury, Caiming Xiong, et al.*2017 · 512 citations*](https://md.research.elamur.ai/free/en/papers/mccann-2017-learned-translation-contextualized-word-vectors/)
98. [**Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time arXiv:2203.05482**Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, et al.*2022 · 507 citations*](https://md.research.elamur.ai/free/en/papers/wortsman-2022-model-soups-averaging-weights-multiple/)
99. **[Sentiment Analysis in the Era of Large Language Models: A Reality Check](https://md.research.elamur.ai/free/en/papers/zhang-2023-sentiment-analysis-era-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenxuan Zhang, Yue Deng, Bing Liu, et al.*2023 · 506 citations*
100. **[Does String-Based Neural MT Learn Source Syntax?](https://md.research.elamur.ai/free/en/papers/shi-2016-does-string-based-neural-mt/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xing Shi, Inkit Padhi, Kevin Knight*2016 · 504 citations*

**1** [2](https://md.research.elamur.ai/free/en/papers/socher-2013-recursive-deep-models-semantic-compositionality/cited-by/page/2/) [3](https://md.research.elamur.ai/free/en/papers/socher-2013-recursive-deep-models-semantic-compositionality/cited-by/page/3/) [4](https://md.research.elamur.ai/free/en/papers/socher-2013-recursive-deep-models-semantic-compositionality/cited-by/page/4/) [next →](https://md.research.elamur.ai/free/en/papers/socher-2013-recursive-deep-models-semantic-compositionality/cited-by/page/2/)

- [← back to](https://md.research.elamur.ai/free/en/papers/socher-2013-recursive-deep-models-semantic-compositionality/) — Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank