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作者：kaiyuan 另外發(fā)現(xiàn)源repo中NLP相關(guān)的綜述不是很多，于是把一些覺(jué)得還不錯(cuò)的文章添加進(jìn)去了，重新整理更新在 AI-Surveys[1] 中。
『收藏等于看完』系列，來(lái)看看都有哪些吧， enjoy！
[1]AI-Surveys: https://github.com/KaiyuanGao/AI-Surveys [2]Recent Trends in Deep Learning Based Natural Language Processing: https:///pdf/1708.02709.pdf [3]Deep Learning Based Text Classification: A Comprehensive Review: https:///pdf/2004.03705 [4]Survey of the SOTA in Natural Language Generation: Core tasks, applications and evaluation: https://www./index.php/jair/article/view/11173/26378 [5]Neural Language Generation: Formulation, Methods, and Evaluation: https:///pdf/2007.15780.pdf [6]Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer:https://ai./2020/02/exploring-transfer-learning-with-t5.html [7]Paper: https:///abs/1910.10683 [8]Neural Transfer Learning for Natural Language Processing: https://aran.library./handle/10379/15463 [9]A Survey on Knowledge Graphs: Representation, Acquisition and Applications: https:///abs/2002.00388 [10]A Survey on Deep Learning for Named Entity Recognition: https:///abs/1812.09449 [11]More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction: https:///abs/2004.03186 [12]Deep Learning for Sentiment Analysis : A Survey: https:///abs/1801.07883 [13]Deep Learning for Aspect-Level Sentiment Classification: Survey, Vision, and Challenges: https://ieeexplore./stamp/stamp.jsp?tp=&arnumber=8726353 [14]Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering: https://www./anthology/C18-1328/ [15]Neural Reading Comprehension And Beyond: https://stacks./file/druid:gd576xb1833/thesis-augmented.pdf [16]Neural Machine Reading Comprehension: Methods and Trends: https:///abs/1907.01118 [17]Neural Machine Translation: A Review: https:///abs/1912.02047 [18]A Survey of Domain Adaptation for Neural Machine Translation: https://www./anthology/C18-1111.pdf [19]Pre-trained Models for Natural Language Processing: A Survey: https:///abs/2003.08271 [20]An Attentive Survey of Attention Models: https:///pdf/1904.02874.pdf [21]An Introductory Survey on Attention Mechanisms in NLP Problems: https:///abs/1811.05544 [22]Attention in Natural Language Processing: https:///abs/1902.02181 [23]A Primer in BERTology: What we know about how BERT works: https:///pdf/2002.12327.pdf [24]Beyond Accuracy: Behavioral Testing of NLP Models with CheckList: https:///pdf/2005.04118.pdf [25]Evaluation of Text Generation: A Survey: https:///pdf/2006.14799.pdf [26]Recommender systems survey: http:///wp-content/uploads/2016/12/sciencedirec.pdf [27]Deep Learning based Recommender System: A Survey and New Perspectives: https:///pdf/1707.07435.pdf [28]Are We Really Making Progress? A Worrying Analysis of Neural Recommendation Approaches: https:///pdf/1907.06902.pdf [29]A Survey of Serendipity in Recommender Systems: https://www./publication/306075233_A_Survey_of_Serendipity_in_Recommender_Systems [30]Diversity in Recommender Systems – A survey: https:///static/memory/pdfs/153-Kunaver_Diversity_in_Recommender_Systems_2017.pdf [31]A Survey of Explanations in Recommender Systems: http://citeseerx.ist./viewdoc/download?doi=10.1.1.418.9237&rep=rep1&type=pdf [32]A State-of-the-Art Survey on Deep Learning Theory and Architectures: https://www./2079-9292/8/3/292/htm [33]Knowledge Distillation: A Survey: https:///pdf/2006.05525.pdf [34]Compression of Deep Learning Models for Text: A Survey: https:///pdf/2008.05221.pdf [35]A Survey on Deep Transfer Learning: https:///pdf/1808.01974.pdf [36]A Comprehensive Survey of Neural Architecture Search-- Challenges and Solutions: https:///abs/2006.02903 [37]Neural Architecture Search: A Survey: https:///abs/1808.05377 [38]Object Detection in 20 Years: https:///pdf/1905.05055.pdf [39]Threat of Adversarial Attacks on Deep Learning in Computer Vision: https://ieeexplore./stamp/stamp.jsp?arnumber=8294186 [40]Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art: https:///pdf/1704.05519.pdf [41]A Brief Survey of Deep Reinforcement Learning: https:///pdf/1708.05866.pdf [42]Transfer Learning for Reinforcement Learning Domains: http://www./papers/volume10/taylor09a/taylor09a.pdf [43]Review of Deep Reinforcement Learning Methods and Applications in Economics: https:///pdf/2004.01509.pdf [44]A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications: https:///pdf/1709.07604 [45]From Word to Sense Embeddings:A Survey on Vector Representations of Meaning: https://www./index.php/jair/article/view/11259/26454 [46]Diachronic Word Embeddings and Semantic Shifts: https:///pdf/1806.03537.pdf [47]Word Embeddings: A Survey: https:///abs/1901.09069 [48]A Survey on Contextual Embeddings: https:///abs/2003.07278 [49]A Survey on Knowledge Graphs: Representation, Acquisition and Applications: https:///abs/2002.00388 [50]Meta-learning for Few-shot Natural Language Processing: A Survey: https:///abs/2007.09604 [51]Learning from Few Samples: A Survey: https:///abs/2007.15484 [52]Meta-Learning in Neural Networks: A Survey: https:///abs/2004.05439 [53]A Comprehensive Overview and Survey of Recent Advances in Meta Learning: https:///abs/2004.11149 [54]Baby steps towards few-shot learning with multiple semantics: https:///abs/1906.01905 [55]Meta-Learning: A Survey: https:///abs/1810.03548 [56]A Perspective View And Survey Of Meta-learning: https://www./publication/2375370_A_Perspective_View_And_Survey_Of_Meta-Learning [57]A Survey on Transfer Learning: http://202.120.39.19:40222/wp-content/uploads/2018/03/A-Survey-on-Transfer-Learning.pdf |
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