25 Must-Read NLP Papers in Deep Learning: A Comprehensive List

Table of Contents

1. Introduction
2. "A Convolutional Neural Network for Modelling Sentences" (2014)
3. "Sequence to Sequence Learning with Neural Networks" (2014)
4. "Recurrent Neural Network Regularization" (2014)
5. "Convolutional Neural Networks for Sentence Classification" (2014)
6. "Neural Machine Translation by Jointly Learning to Align and Translate" (2014)
7. "Neural Turing Machines" (2014)
8. "Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation" (2014)
9. "Bidirectional LSTM-CRF Models for Sequence Tagging" (2015)
10. "Deep Learning for Natural Language Processing" (2015)
11. "Character-Aware Neural Language Models" (2015)
12. "Pointer Networks" (2015)
13. "Semi-Supervised Learning with Ladder Networks" (2015)
14. "Meta-Learning with Memory-Augmented Neural Networks" (2016)
15. "Attention is All You Need" (2017)
16. "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" (2018)
17. "GPT-2: Language Models are Unsupervised Multitask Learners" (2019)
18. "ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators" (2020)
19. "T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer" (2020)
20. "A Simple Framework for Contrastive Learning of Visual Representations" (2020)
21. "Vision Transformer (ViT): An Image Transformer" (2020)
22. "MT5: A Massively Multilingual Pre-trained Text-to-Text Transformer" (2020)
23. "Unified Language Model Pre-training for Natural Language Understanding and Generation" (2021)
24. "Plug and Play Language Models: A Simple Approach to Controlled Text Generation" (2021)
25. "The Unreasonable Effectiveness of Transformer Language Models in Grammatical Error Correction" (2022)
26. "Knowledge Enhanced Hierarchical Attention Network for Document Classification" (2022)
27. Conclusion

Introduction

• Natural Language Processing (NLP) has come a long way since the early days of rule-based systems. Deep Learning (DL) has revolutionized the field and enabled researchers to build highly accurate and sophisticated models for a wide range of NLP tasks.

• In this article at OpenGenus, we will list some of the must-read research papers in the field of NLP that have had a significant impact on the development of deep learning models for NLP tasks.

1."A Convolutional Neural Network for Modelling Sentences" (2014)

Authors: Nal Kalchbrenner, Edward Grefenstette, and Phil Blunsom

Summary: This paper proposes a convolutional neural network (CNN) architecture for sentence modeling. The model uses one-dimensional convolutions over sentence embeddings to learn hierarchical features.

Impact: This paper introduced a novel approach for modeling sentences that achieved state-of-the-art results on several benchmark datasets, including the Stanford Sentiment Treebank and the Large Movie Review Dataset.

Year Published: 2014

Published Where: ACL Anthology

2."Sequence to Sequence Learning with Neural Networks" (2014)

Authors: Ilya Sutskever, Oriol Vinyals, and Quoc V. Le

Summary: This paper introduces sequence-to-sequence learning with neural networks, a technique for training models to generate output sequences given input sequences. The paper proposes an encoder-decoder architecture for this task, where the encoder processes the input sequence and generates a fixed-length context vector, which is then used by the decoder to generate the output sequence.

Impact: This paper has been influential in the development of many sequence generation applications, including machine translation, speech recognition, and image captioning.

Year Published: 2014

Published Where: NIPS

3."Recurrent Neural Network Regularization" (2014)

Authors: Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals

Summary: This paper introduced two new techniques for regularizing RNNs: dropout and weight tying. Dropout is a popular technique in deep learning for preventing overfitting, while weight tying encourages weight sharing between the forward and backward directions of a bidirectional RNN.

Impact: The paper demonstrated that these techniques could significantly improve the performance of RNN models on language modeling tasks, and has since been cited extensively in the NLP community.

Year Published: 2014

Published Where: arXiv

4."Convolutional Neural Networks for Sentence Classification" (2014)

Authors: Yoon Kim

Summary: This paper introduced a new model architecture for sentence classification using convolutional neural networks (CNNs). The model applies multiple filters of different sizes over the input sentence, which are then max-pooled to obtain a fixed-length sentence representation.

Impact: This paper showed that CNNs can be effective for sentence classification tasks and inspired further research on applying CNNs to other NLP tasks.

Year Published: 2014

Published Where: Conference on Empirical Methods in Natural Language Processing (EMNLP)

5."Neural Machine Translation by Jointly Learning to Align and Translate" (2014)

Authors: Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio

Summary: This paper introduced a new approach to neural machine translation that learns to align and translate words between source and target languages simultaneously. The model uses an attention mechanism to learn to focus on different parts of the source sentence during translation.

Impact: This paper introduced a new paradigm for neural machine translation that has since become the dominant approach, outperforming traditional statistical machine translation systems.

Year Published: 2014

Published Where: Conference on Neural Information Processing Systems (NIPS)

6."Neural Turing Machines" (2014)

Authors: Alex Graves, Greg Wayne, and Ivo Danihelka (Google DeepMind)

Summary: This paper proposes a type of neural network called Neural Turing Machines (NTMs) that augment standard recurrent neural networks (RNNs) with external memory, allowing the network to learn and store information in a way that mimics a Turing machine. The paper demonstrates the effectiveness of NTMs on tasks such as sorting, associative recall, and algorithmic tasks.

Impact: This paper presents a significant advancement in the field of neural networks and serves as a key example of how external memory can enhance the capabilities of recurrent neural networks.

Year published: 2014

Published where: arXiv

7."Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation" (2014)

Authors: Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio

Summary: This paper introduced the use of RNN Encoder-Decoder models for statistical machine translation, which have since become a widely used approach for neural machine translation. The paper proposed a new way of representing sentences as fixed-length vectors, called "thought vectors", which have been found to be useful in a variety of NLP tasks.

Impact: This paper paved the way for the development of more advanced neural machine translation models and sparked a lot of research on sequence-to-sequence models.

Year Published: 2014

Published Where: Conference on Empirical Methods in Natural Language Processing (EMNLP)

8."Bidirectional LSTM-CRF Models for Sequence Tagging" (2015)

Authors: Zhiheng Huang, Wei Xu, and Kai Yu

Summary: This paper introduces a sequence labeling model that combines bidirectional LSTM (long short-term memory) with conditional random fields (CRF). The model is capable of handling various sequence tagging tasks, including named entity recognition and part-of-speech tagging.

Impact: Bidirectional LSTM-CRF has achieved state-of-the-art performance on several benchmark datasets, including CoNLL 2003 and OntoNotes 5.0.

Year Published: 2015

Published Where: ACL Anthology

9."Deep Learning for Natural Language Processing" (2015)

Authors: Palash Goyal, Sumit Pandey, and Karan Jain

Summary: This paper provides a comprehensive review of deep learning techniques for natural language processing (NLP). It covers various aspects of NLP, including sentiment analysis, machine translation, and question answering.

Impact: This paper has become a popular reference for researchers and practitioners working in the field of NLP, providing an overview of the state-of-the-art techniques and their applications.

Year Published: 2015

Published Where: arXiv

10."Character-Aware Neural Language Models" (2015)

Authors: Yoon Kim, Yacine Jernite, David Sontag, and Alexander Rush

Summary: This paper introduced a character-aware neural language model that combines word-level and character-level representations to improve the performance of language models. The model learns to represent words as a combination of character embeddings and a learned word-level representation.

Impact: This paper demonstrated that character-level information can improve the performance of neural language models, which has since become a popular technique for improving the performance of NLP models on low-resource languages and out-of-vocabulary words.

Year Published: 2015

Published Where: Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)

11."Pointer Networks" (2015)

Authors: Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly (Google DeepMind)

Summary: This paper introduces Pointer Networks, a type of neural network that can output sequences of variable length by pointing to elements in an input sequence instead of generating them directly. The authors demonstrate the effectiveness of Pointer Networks on a variety of tasks, including sorting, shortest path calculation, and convex hull generation.

Impact: This paper presents a novel approach to sequence generation that is more flexible than traditional methods and has potential applications in a wide range of fields.

Year published: 2015

Published where: arXiv

12."Semi-Supervised Learning with Ladder Networks" (2015)

Authors: Antti Rasmus, Harri Valpola, Mikko Honkala, Mathias Berglund, and Tapani Raiko (Google)

Summary: This paper introduces Ladder Networks, a deep neural network architecture for semi-supervised learning, which can improve the accuracy of supervised learning with only a small amount of labeled data by leveraging unlabeled data. Ladder Networks use a novel combination of supervised and unsupervised learning with the help of a hidden "clean" layer that acts as a denoising autoencoder to reconstruct inputs from noisy versions.

Impact: Ladder Networks have been shown to outperform other semi-supervised learning approaches on several benchmark datasets, including MNIST, CIFAR-10, and SVHN. The paper has been cited over 1,000 times, and Ladder Networks have become a popular method for semi-supervised learning in deep neural networks.

Year published: 2015

Published where: Advances in Neural Information Processing Systems (NIPS)

13."Meta-Learning with Memory-Augmented Neural Networks" (2016)

Authors: Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap (DeepMind)

Summary: This paper proposes a meta-learning algorithm based on memory-augmented neural networks (MANNs). The algorithm is designed to learn quickly from new tasks by leveraging previously acquired knowledge, which is stored in a memory bank. The MANN architecture combines a neural network with an external memory module that can be read and written to using attention mechanisms.

Impact: The MANN algorithm has been shown to achieve state-of-the-art results on several few-shot learning benchmarks, where the goal is to learn quickly from a small amount of labeled data. The paper has also motivated further research on combining neural networks with external memory to improve their ability to learn and reason.

Year published: 2016

Published where: Proceedings of the 33rd International Conference on Machine Learning (ICML)

14."Attention is All You Need" (2017)

Authors: Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin (Google Brain, Google, Carnegie Mellon University)

Summary: The paper introduces the Transformer architecture, which utilizes self-attention to model relationships between different positions in a sequence of input data. The model does not require recurrent or convolutional layers, resulting in faster training and better performance on various natural language processing tasks.

Impact: The Transformer model has become the backbone of numerous state-of-the-art natural language processing models, including GPT-2, BERT, and T5. As of April 2023, the paper has over 16,000 citations.

Year published: 2017

Published where: NIPS

15."BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" (2018)

Authors: Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova (Google AI Language, Google)

Summary: The paper presents BERT, a pre-trained language model based on the Transformer architecture that learns contextual relations between words in a sentence. The model can be fine-tuned for various downstream NLP tasks, achieving state-of-the-art performance on many of them.

Impact: BERT has become one of the most widely used pre-trained language models, outperforming previous approaches on a wide range of NLP tasks. As of April 2023, the paper has over 11,000 citations.

Year published: 2018

Published where: ACL

16."GPT-2: Language Models are Unsupervised Multitask Learners" (2019)

Authors: Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever (OpenAI)

Summary: The paper introduces GPT-2, a large-scale pre-trained language model that can generate high-quality text in a variety of styles and genres. The model is trained on a diverse set of web pages and can perform well on a wide range of natural language processing tasks with few additional task-specific parameters.

Impact: GPT-2 has demonstrated state-of-the-art performance on several NLP benchmarks and has been used for a wide range of applications, including text generation and question-answering. As of April 2023, the paper has over 1,800 citations.

Year published: 2019

Published where: arXiv

17."ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators" (2020)

Authors: Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning (Google Brain, Stanford University)

Summary: The paper introduces ELECTRA, a pre-training approach for language models that trains the model as a discriminator to distinguish between real and fake input, rather than as a generator. The approach improves the efficiency and effectiveness of pre-training compared to existing models like BERT.

Impact: ELECTRA has achieved state-of-the-art results on several natural language understanding and generation tasks while requiring fewer pre-training steps and computation compared to previous approaches. As of April 2023, the paper has over 1,000 citations.

Year published: 2020

Published where: ICLR

18."T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer" (2020)

Authors: Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu

Summary: T5 is a text-to-text transformer that is capable of handling a wide range of tasks, including language translation, summarization, question answering, and more. It employs a simple and consistent architecture that allows for efficient training and fine-tuning.

Impact: T5 is a powerful language model that has set state-of-the-art results on several benchmarks, including the SuperGLUE benchmark and the COCO Captioning dataset.

Year Published: 2020

Published Where: arXiv

19."A Simple Framework for Contrastive Learning of Visual Representations" (2020)

Authors: Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton (Google Research)

Summary: This paper proposes a simple framework for contrastive learning of visual representations, called SimCLR. The authors demonstrate that SimCLR can learn high-quality visual representations from large amounts of unlabeled data and that these representations can be fine-tuned for specific downstream tasks with a small amount of labeled data.

Impact: This paper has contributed to the recent surge of interest in self-supervised learning and contrastive learning, which have shown promise in achieving state-of-the-art performance on various computer vision tasks.

Year published: 2020

Published where: arXiv

20."Vision Transformer (ViT): An Image Transformer" (2020)

Authors: Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Matthias Dehghani, Marenlinch Zhang, and Alaaeldin El-Nouby (Google Research)

Summary: This paper proposes a transformer-based model for image classification, called the Vision Transformer (ViT). Unlike traditional convolutional neural networks, which rely on handcrafted features, ViT uses self-attention to directly process the raw image pixels. The authors show that ViT achieves competitive performance on several benchmark datasets.

Impact: This paper has demonstrated the potential of transformer-based models for computer vision tasks, paving the way for future research on hybrid models that combine the strengths of both convolutional neural networks and transformers.

Year published: 2020

Published where: arXiv

21."MT5: A Massively Multilingual Pre-trained Text-to-Text Transformer" (2020)

Authors: Patrick Lewis, Barney Pell, Matt Gardner, Christopher Clark, Mark Neumann, and Johan Uszkoreit (Google Research)

Summary: This paper introduces MT5, a massively multilingual pre-trained text-to-text transformer model. MT5 is trained on a diverse set of tasks and languages, and can perform tasks such as text classification, summarization, and translation in over 100 different languages. The authors evaluate MT5 on several benchmark datasets and show that it achieves state-of-the-art results on many of these tasks.

Impact: MT5 is a highly versatile and effective pre-trained model for text-to-text tasks in a wide range of languages. Its high performance on multiple benchmark datasets demonstrates its potential for various NLP applications.

Year published: 2020

Published where: arXiv preprint

22."Unified Language Model Pre-training for Natural Language Understanding and Generation" (2021)

Authors: Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu (Google Brain)

Summary: This paper introduces the Unified Language Model (UniLM), which is a general-purpose language model that can be fine-tuned for various NLP tasks, including text classification, question answering, and machine translation. UniLM is based on a transformer architecture and can process input sequences of varying lengths.

Impact: UniLM has achieved state-of-the-art results on multiple NLP benchmarks, including the GLUE benchmark, which is a collection of diverse NLP tasks. The paper has also inspired further research on developing universal language models that can perform multiple tasks without the need for task-specific models.

Year published: 2021

Published where: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)

23."Plug and Play Language Models: A Simple Approach to Controlled Text Generation" (2021)

Authors: Ethan Perez, Patrick Lewis, and Matt Gardner (Google Research)

Summary: This paper proposes a simple approach to controlled text generation using plug and play language models (PPLMs). PPLMs combine a pre-trained language model with a learned conditioning input to generate text that adheres to a certain style or topic. The authors demonstrate the effectiveness of PPLMs on several tasks, including dialogue generation and sentiment modification.

Impact: PPLMs provide a straightforward method for generating text with controlled attributes, such as sentiment or topic. Their simplicity and effectiveness make them a promising approach for various applications, including chatbots and content generation.

Year published: 2021

Published where: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021)

24."The Unreasonable Effectiveness of Transformer Language Models in Grammatical Error Correction" (2022)

Authors: Ziang Xie, Jinchao Zhang, and Xiaojun Wan (Peking University)

Summary: This paper investigates the use of transformer-based language models for grammatical error correction (GEC). The authors compare several pre-trained transformer models, including GPT-2 and BERT, and show that fine-tuning them on a GEC task leads to significant improvements in performance.

Impact: The paper demonstrates the effectiveness of transformer-based language models for GEC, which is a challenging task due to the diversity of errors and the need for context-sensitive corrections. The results suggest that pre-trained language models can be an effective starting point for developing GEC systems.

Year published: 2022

Published where: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL)

25."Knowledge Enhanced Hierarchical Attention Network for Document Classification" (2022)

Authors: Xiaochen Zhang, Yikai Wang, Guohua Bai, and Yanan Lu (Chinese Academy of Sciences)

Summary: This paper proposes a knowledge-enhanced hierarchical attention network (KEHAN) for document classification that can leverage both the structural information of a document and external knowledge. The model consists of a hierarchical attention network to capture the structural information of a document and a knowledge-enhanced attention mechanism to incorporate external knowledge. The authors evaluate the proposed model on three different datasets, and the results demonstrate that KEHAN outperforms several baseline methods on all the datasets.

Impact: KEHAN is an effective approach for document classification that can leverage external knowledge to improve performance. The incorporation of a hierarchical attention network and a knowledge-enhanced attention mechanism results in better accuracy compared to several baseline models.

Year published: 2022

Published where: Proceedings of the 33rd International Conference on Computational Linguistics (COLING 2022)

Conclusion

• The field of NLP has witnessed significant progress in recent years, with deep learning models being at the forefront of this advancement. In this article, we have listed some of the must-read research papers in the field of NLP that have had a significant impact on the development of deep learning models for NLP tasks.

• The papers we have discussed cover a wide range of NLP tasks, including machine translation, language modeling, sentiment analysis, and sequence tagging. They have introduced innovative approaches to address some of the most pressing challenges in the field, such as long-term dependency modeling, sequence labeling, and modeling word relationships in a sentence.

• These papers have not only proposed state-of-the-art models but also inspired further research and development in the field of NLP. Many of them have been widely cited and have become standard references in the field. We hope this list serves as a helpful guide for those looking to explore the latest research in NLP and to better understand the advancements that have been made in this exciting field.