Categorization We follow the ACL and ICML submission guideline of recent years, covering a broad range of areas in NLP and ML. The categorization is as follows: Natural Language Processing Computational Social Science and Social Media Dialogue and Interactive Systems Generation Information Extraction Information Retrieval and Text Mining Interpretability and Analysis of Models for NLP Knowledge Graph Language Grounding to Vision, Robotics and Beyond Linguistic Theories, Cognitive Modeling and Psycholinguistics Machine Learning for NLP Machine Translation Natural Language Processing Named Entity Recognition (NER) NLP Applications Question Answering Reading Comprehension Recommender Systems Resources and Evaluation Semantics Sentiment Analysis, Stylistic Analysis, and Argument Mining Speech and Multimodality Summarization Syntax: Tagging, Chunking, Syntax and Parsing Text Classification Machine Learning Architectures AutoML Bayesian Methods Classification,Clustering,Regression Curriculum Learning Data Augmentation Deep Learning - General Methods Deep Reinforcement Learning Federated Learning Few-Shot and Zero-Shot Learning General Machine Learning Generative Adversarial Networks Graph Neural Networks Interpretability and Analysis Meta Learning Metric Learning ML Applications Model Compression and Acceleration Multi-Task and Multi-View Learning Online Learning Optimization Semi-Supervised and Unsupervised Learning Transfer Learning Trustworthy Machine Learning github地址： https://github.com/NiuTrans/ABigSurvey