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https://github.com/KaiyuanGao/AI-Surveys

整理AI相关领域的一些综述
https://github.com/KaiyuanGao/AI-Surveys

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整理AI相关领域的一些综述

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# AI-Surveys



本repo主要整理AI相关领域的一些综述，起因是看到了 [ml-survey](https://github.com/eugeneyan/ml-surveys) 这个非常棒的项目。



目前添加了『自然语言处理』模块的部分觉得不错的综述。



欢迎有兴趣的小伙伴们一起整理。



**Table of Contents**



- [自然语言处理 (NLP)](#自然语言处理nlp)

- [推荐系统 (Recommender System)](#推荐系统recommender-system)

- [深度学习 (Deep Learning)](#深度学习deep-learning-1)

- [计算机视觉 (Computer Vision)](#计算机视觉computer-vision)

- [图网络 (Graph Network)](#图网络graph-network)

- [强化学习 (Reinforcement Learning)](#强化学习reinforcement-learning)

- [向量化 (Embeddings)](#向量化embeddings)

- [多任务学习 (Multi-Task Learning)](#多任务学习multi-task-learning)

- [Meta-learning & Few-shot Learning](#meta-learning--few-shot-learning)

- [搜索推荐](#搜索推荐)

- [Others](#其他others-1)



## 自然语言处理(NLP)

#### 文本分类(Text Classification)



- [Deep Learning Based Text Classification: A Comprehensive Review](https://arxiv.org/pdf/2004.03705 "Deep Learning Based Text Classification: A Comprehensive Review")

- [A Survey on Data Augmentation for Text Classification](https://arxiv.org/pdf/2107.03158.pdf)

#### 情感分析(Sentiment Analysis)



- [Deep Learning for Sentiment Analysis : A Survey](https://arxiv.org/abs/1801.07883)

- [Deep Learning for Aspect-Level Sentiment Classification: Survey, Vision, and Challenges](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8726353)



#### 命名实体识别(Named Entity Recognition)



- [A Survey on Deep Learning for Named Entity Recognition](https://arxiv.org/abs/1812.09449 "A Survey on Deep Learning for Named Entity Recognition")



#### 关系抽取(Relation Extraction)



- [More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction](https://arxiv.org/abs/2004.03186 "More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction")

- [A Survey of Deep Learning Methods for Relation Extraction](https://link.zhihu.com/?target=https%3A//arxiv.org/pdf/1705.03645.pdf)



#### 文本匹配(Text Matching)



- [Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering](https://www.aclweb.org/anthology/C18-1328/)

- [深度文本匹配综述](http://d.wanfangdata.com.cn/periodical/jsjxb201704014)

- [Pretrained Transformers for Text Ranking: BERT and Beyond](https://arxiv.org/pdf/2010.06467.pdf)



#### 阅读理解(Reading Comprehension)



- [Neural Reading Comprehension And Beyond](https://stacks.stanford.edu/file/druid:gd576xb1833/thesis-augmented.pdf)

- [Neural Machine Reading Comprehension: Methods and Trends](https://arxiv.org/abs/1907.01118)

- [Machine Reading Comprehension: The Role of Contextualized Language Models and Beyond](https://arxiv.org/abs/2005.06249)

- [Machine Reading Comprehension: a Literature Review](https://arxiv.org/abs/1907.01686)



#### 机器翻译(Machine Translation)



- [Neural Machine Translation: A Review](https://arxiv.org/abs/1912.02047)

- [A Survey of Domain Adaptation for Neural Machine Translation](https://www.aclweb.org/anthology/C18-1111.pdf)

- [Neural Machine Translation: Challenges, Progress and Future](https://arxiv.org/abs/2004.05809v1)

- [A Survey of Methods to Leverage Monolingual Data in Low-resource Neural Machine Translation ](https://arxiv.org/abs/1910.00373)

- [A Comprehensive Survey of Multilingual Neural Machine Translation](https://arxiv.org/abs/2001.01115)

- [A Survey of Deep Learning Techniques for Neural Machine Translation ](https://arxiv.org/abs/2002.07526)

- [A Survey on Document-level Machine Translation: Methods and Evaluation](https://arxiv.org/abs/1912.08494)



#### 文本生成(Text Generation)



- [A Survey of Knowledge-Enhanced Text Generation](https://arxiv.org/abs/2010.04389)

- [Neural Language Generation: Formulation, Methods, and Evaluation](https://arxiv.org/pdf/2007.15780.pdf "Neural Language Generation: Formulation, Methods, and Evaluation")

-  [Survey of the SOTA in Natural Language Generation: Core tasks, applications and evaluation](https://www.jair.org/index.php/jair/article/view/11173/26378 "Survey of the SOTA in Natural Language Generation: Core tasks, applications and evaluation")

- [Evaluation of Text Generation: A Survey](https://arxiv.org/pdf/2006.14799.pdf "Evaluation of Text Generation: A Survey")

- [Recent Advances in Neural Question Generation](https://link.zhihu.com/?target=https%3A//arxiv.org/abs/1905.08949)

- [Neural Text Generation: Past, Present and Beyond](https://arxiv.org/pdf/1803.07133.pdf)

- [Pretrained Language Models for Text Generation: A Survey](https://arxiv.org/abs/2105.10311)



#### 摘要抽取(Abstractive Summarization)



- [Abstractive Summarization: A Survey of the State of the Art](https://aaai.org/ojs/index.php/AAAI/article/view/5056/4929)



#### 对话系统(Dialog System)



- [Recent Advances and Challenges in Task-oriented Dialog System](https://arxiv.org/abs/2003.07490)

- [Neural Approaches to Conversational AI](https://arxiv.org/abs/1809.08267)

- [Challenges in Building Intelligent Open-domain Dialog Systems](https://arxiv.org/abs/1905.05709)

- [How Far are We from Effective Context Modeling? An Exploratory Study on Semantic Parsing in Context](https://arxiv.org/pdf/2002.00652.pdf)

- [A Survey on Dialogue Summarization: Recent Advances and New Frontiers](https://arxiv.org/abs/2107.03175)



#### 知识图谱(Knowledge Graph)



- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://arxiv.org/abs/2002.00388 "A Survey on Knowledge Graphs: Representation, Acquisition and Applications")

- [Core techniques of question answering systems over knowledge bases: a survey](http://wdaqua.eu/assets/publications/2017_KAIS.pdf)

- [Introduction to Neural Network based Approaches for Question Answering over Knowledge Graphs](https://arxiv.org/abs/1907.09361)



#### 深度学习(Deep Learning)



-  [Recent Trends in Deep Learning Based Natural Language Processing](https://arxiv.org/pdf/1708.02709.pdf "Recent Trends in Deep Learning Based Natural Language Processing")

- [A Survey of the Usages of Deep Learning in Natural Language Processing](https://arxiv.org/abs/1807.10854)



#### 迁移学习(Transfer Learning)



- [Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html "Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer") ([Paper](https://arxiv.org/abs/1910.10683 "Paper"))

- [Neural Transfer Learning for Natural Language Processing](https://aran.library.nuigalway.ie/handle/10379/15463)



#### 预训练模型(Pre-trained Models)



- [Pre-trained Models for Natural Language Processing: A Survey](https://arxiv.org/abs/2003.08271 "Pre-trained Models for Natural Language Processing: A Survey")

- [A Primer in BERTology: What we know about how BERT works](https://arxiv.org/pdf/2002.12327.pdf)



#### 注意力机制(Attention Mechanism)



- [An Attentive Survey of Attention Models](https://arxiv.org/pdf/1904.02874.pdf)

- [An Introductory Survey on Attention Mechanisms in NLP Problems](https://arxiv.org/abs/1811.05544)

- [Attention in Natural Language Processing](https://arxiv.org/abs/1902.02181)



#### 其他(Others)



- [A Survey of Data Augmentation Approaches for NLP](https://arxiv.org/abs/2105.03075)

- [Beyond Accuracy: Behavioral Testing of NLP Models with CheckList](https://arxiv.org/pdf/2005.04118.pdf "Beyond Accuracy: Behavioral Testing of NLP Models with CheckList")

- [Analysis Methods in Neural Language Processing: A Survey](https://www.aclweb.org/anthology/Q19-1004.pdf)

-  [A Primer on Neural Network Models for Natural Language Processing ](https://arxiv.org/pdf/1510.00726.pdf)



## 推荐系统(Recommender System)

- [A Survey on Neural Recommendation: From Collaborative Filtering to Content and Context Enriched Recommendation](https://arxiv.org/abs/2104.13030)

- [Recommender systems survey](http://irntez.ir/wp-content/uploads/2016/12/sciencedirec.pdf "Recommender systems survey")

- [Deep Learning based Recommender System: A Survey and New Perspectives](https://arxiv.org/pdf/1707.07435.pdf "Deep Learning based Recommender System: A Survey and New Perspectives")

- [Are We Really Making Progress? A Worrying Analysis of Neural Recommendation Approaches](https://arxiv.org/pdf/1907.06902.pdf "Are We Really Making Progress? A Worrying Analysis of Neural Recommendation Approaches")

- [A Survey of Serendipity in Recommender Systems](https://www.researchgate.net/publication/306075233_A_Survey_of_Serendipity_in_Recommender_Systems "A Survey of Serendipity in Recommender Systems")

- [Diversity in Recommender Systems – A survey](https://papers-gamma.link/static/memory/pdfs/153-Kunaver_Diversity_in_Recommender_Systems_2017.pdf "Diversity in Recommender Systems – A survey")

- [A Survey of Explanations in Recommender Systems](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.418.9237&rep=rep1&type=pdf "A Survey of Explanations in Recommender Systems")

- [A Survey on Knowledge Graph-Based Recommender Systems](https://arxiv.org/abs/2003.00911)

- [Adversarial Machine Learning in Recommender Systems: State of the art and Challenges](https://www.semanticscholar.org/paper/Adversarial-Machine-Learning-in-Recommender-State-Deldjoo-Noia/ea5bf8ec238203da77cd43229c386204abb7717c)

- [Graph Learning Approaches to Recommender Systems: A Review](https://arxiv.org/abs/2004.11718)



## 深度学习(Deep Learning)

- [A State-of-the-Art Survey on Deep Learning Theory and Architectures](https://www.mdpi.com/2079-9292/8/3/292/htm "A State-of-the-Art Survey on Deep Learning Theory and Architectures")

- 知识蒸馏：[Knowledge Distillation: A Survey](https://arxiv.org/pdf/2006.05525.pdf "Knowledge Distillation: A Survey")

- 模型压缩： [Compression of Deep Learning Models for Text: A Survey](https://arxiv.org/pdf/2008.05221.pdf "Compression of Deep Learning Models for Text: A Survey")

- 迁移学习： [A Survey on Deep Transfer Learning](https://arxiv.org/pdf/1808.01974.pdf "A Survey on Deep Transfer Learning")

- 神经架构搜索： [A Comprehensive Survey of Neural Architecture Search-- Challenges and Solutions](https://arxiv.org/abs/2006.02903 "A Comprehensive Survey of Neural Architecture Search-- Challenges and Solutions")

- 神经架构搜索： [Neural Architecture Search: A Survey](https://arxiv.org/abs/1808.05377 "Neural Architecture Search: A Survey")



## 计算机视觉(Computer Vision)

- 目标检测： [Object Detection in 20 Years](https://arxiv.org/pdf/1905.05055.pdf "Object Detection in 20 Years")

- 对抗性攻击：[Threat of Adversarial Attacks on Deep Learning in Computer Vision](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8294186 "Threat of Adversarial Attacks on Deep Learning in Computer Vision")

- 自动驾驶：[Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art](https://arxiv.org/pdf/1704.05519.pdf "Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art")



## 图网络(Graph Network)

- [Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey](https://arxiv.org/abs/2005.07496)

- [Introduction to Graph Neural Networks](https://www.morganclaypool.com/doi/10.2200/S00980ED1V01Y202001AIM045)

- [A Practical Guide to Graph Neural Networks](https://arxiv.org/pdf/2010.05234.pdf)

- [Graph Neural Networks: A Review of Methods and Applications](https://arxiv.org/pdf/1812.08434.pdf)

- [A Comprehensive Survey on Graph Neural Networks](https://arxiv.org/pdf/1901.00596.pdf)

- [Deep Learning on Graphs: A Survey](https://arxiv.org/pdf/1812.04202.pdf)

- [Adversarial Attack and Defense on Graph Data: A Survey](https://arxiv.org/pdf/1812.10528.pdf)

- [The Graph Neural Network Model](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4700287)

- [Benchmarking Graph Neural Networks](https://arxiv.org/pdf/2003.00982.pdf)



## 强化学习(Reinforcement Learning)

- [A Brief Survey of Deep Reinforcement Learning](https://arxiv.org/pdf/1708.05866.pdf "A Brief Survey of Deep Reinforcement Learning")

-  [Transfer Learning for Reinforcement Learning Domains](http://www.jmlr.org/papers/volume10/taylor09a/taylor09a.pdf "Transfer Learning for Reinforcement Learning Domains")

-  [Review of Deep Reinforcement Learning Methods and Applications in Economics](https://arxiv.org/pdf/2004.01509.pdf "Review of Deep Reinforcement Learning Methods and Applications in Economics")



## 向量化(Embeddings)

- 图： [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://arxiv.org/pdf/1709.07604 "A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications")

- 文本： [From Word to Sense Embeddings:A Survey on Vector Representations of Meaning](https://www.jair.org/index.php/jair/article/view/11259/26454 "From Word to Sense Embeddings:A Survey on Vector Representations of Meaning")

- 文本： [Diachronic Word Embeddings and Semantic Shifts](https://arxiv.org/pdf/1806.03537.pdf "Diachronic Word Embeddings and Semantic Shifts")

- 文本： [Word Embeddings: A Survey](https://arxiv.org/abs/1901.09069 "Word Embeddings: A Survey")

- [A Survey on Contextual Embeddings](https://arxiv.org/abs/2003.07278 "A Survey on Contextual Embeddings")



## 多任务学习(Multi-Task Learning)

- [Multi-Task Learning for Dense Prediction Tasks: A Survey](https://arxiv.org/abs/2004.13379)

- [An overview of multi-task learning](https://academic.oup.com/nsr/article/5/1/30/4101432)

- [A Survey on Multi-Task Learning](https://arxiv.org/abs/1707.08114)

- [Multi-Task Learning with Deep Neural Networks: A Survey](https://arxiv.org/abs/2009.09796)

- [A Comparison of Loss Weighting Strategies for Multi task Learning in Deep Neural Networks](https://ieeexplore.ieee.org/document/8848395)

- [Multi-task learning for natural language processing in the 2020s: Where are we going?](https://www.sciencedirect.com/science/article/abs/pii/S0167865520302087?via%3Dihub)

- [Empirical Evaluation of Multi-task Learning in Deep Neural Networks for Natural Language Processing](https://link.springer.com/article/10.1007/s00521-020-05268-w)



## Meta-learning & Few-shot Learning

- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://arxiv.org/abs/2002.00388 "A Survey on Knowledge Graphs: Representation, Acquisition and Applications")

- [Meta-learning for Few-shot Natural Language Processing: A Survey](https://arxiv.org/abs/2007.09604 "Meta-learning for Few-shot Natural Language Processing: A Survey")

- [Learning from Few Samples: A Survey](https://arxiv.org/abs/2007.15484 "Learning from Few Samples: A Survey")

- [Meta-Learning in Neural Networks: A Survey](https://arxiv.org/abs/2004.05439 "Meta-Learning in Neural Networks: A Survey")

- [A Comprehensive Overview and Survey of Recent Advances in Meta-Learning](https://arxiv.org/abs/2004.11149 "A Comprehensive Overview and Survey of Recent Advances in Meta-Learning")

- [Baby steps towards few-shot learning with multiple semantics](https://arxiv.org/abs/1906.01905 "Baby steps towards few-shot learning with multiple semantics")

- [Meta-Learning: A Survey](https://arxiv.org/abs/1810.03548 "Meta-Learning: A Survey")

- [A Perspective View And Survey Of Meta-learning](https://www.researchgate.net/publication/2375370_A_Perspective_View_And_Survey_Of_Meta-Learning "A Perspective View And Survey Of Meta-learning")



## 搜索推荐



- [Deep Learning based Recommender System: A Survey and New Perspectives](https://arxiv.org/abs/1707.07435)

- [A Survey on Knowledge Graph-Based Recommender Systems](https://arxiv.org/abs/2003.00911)

- [Graph Learning Approaches to Recommender Systems: A Review](https://arxiv.org/abs/2004.11718)

- [Adversarial Machine Learning in Recommender Systems: State of the art and Challenges](https://www.semanticscholar.org/paper/Adversarial-Machine-Learning-in-Recommender-State-Deldjoo-Noia/ea5bf8ec238203da77cd43229c386204abb7717c)

- [Graph Neural Networks in Recommender Systems: A Survey](https://arxiv.org/abs/2011.02260)

- 



## 其他（Others）



- [A Survey on Transfer Learning](http://202.120.39.19:40222/wp-content/uploads/2018/03/A-Survey-on-Transfer-Learning.pdf "A Survey on Transfer Learning")