- Natural Language Processing Advancements By Deep Learning: A Survey
- Pre-trained Models for Natural Language Processing: A Survey
- Analysis Methods in Neural Language Processing: A Survey
- A Survey on Natural Language Processing for Fake News Detection
- Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches
- A Review of the Neural History of Natural Language Processing
- A Survey of the State-of-the-Art Language Models up to Early 2020
- Trends in Integration of Vision and Language Research: A Survey of Tasks, Datasets, and Methods
- Current Limitations of Language Models: What You Need is Retrieval
- NiuTrans/ABigSurvey
- https://gluebenchmark.com/leaderboard
- https://ruder.io/
- frontiers-of-natural-language-processing
- 2019 — Year of BERT and Transformer
- FROM Pre-trained Word Embeddings TO Pre-trained Language Models — Focus on BERT
- Deep Learning, Nature 2015
- LSTM: A Search Space Odyssey, arXiv:1503.04069
- A Critical Review of Recurrent Neural Networks for Sequence Learning, arXiv:1506.00019
- Visualizing and Understanding Recurrent Networks, arXiv:1506.02078
- An Empirical Exploration of Recurrent Network Architectures, ICML, 2015.
- Recent Advances in Recurrent Neural Networks. 2018. [arXiv]
- From Nodes to Networks: Evolving Recurrent Neural Networks. 2018. [arXiv]
- The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches. 2018. [arXiv]
- Natural Language Processing: State of The Art, Current Trends and Challenges
- Recent Trends in Deep Learning Based Natural Language Processing
- An Introductory Survey on Attention Mechanisms in NLP Problems
- learning-path-nlp-2020
