# AI-Surveys **Repository Path**: chenyang3817/AI-Surveys ## Basic Information - **Project Name**: AI-Surveys - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2021-05-27 - **Last Updated**: 2021-05-27 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # 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。 ## 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") #### 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) #### 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) - [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) - [Analysis Methods in Neural Language Processing: A Survey](https://www.mitpressjournals.org/doi/full/10.1162/tacl_a_00254) ## 推荐系统(Recommender System) - [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") ## 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) - ## 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")