apache的专栏

ELMo：Deep contextualized word representations_ywm_up的博客-优快云博客

NLP问题ref：Speech and Language Processing (3rd ed. draft),December 30, 2020 draft,https://web.stanford.edu/~jurafsky/slp3/深度学习解决NLP问题：语义相似度计算https://www.cnblogs.com/qniguoym/p/7772561.htmlNLP词法，语义分析https://blog.youkuaiyun.com/weixin_41657760/catego

Switch Transformer

Chatbot: Microsoft XiaoIceThe Design and Implementation of XiaoIce, an Empathetic Social Chatbot,2018https://arxiv.org/abs/1812.08989https://mp.weixin.qq.com/s/wBsh9dmMPks04X2pDB8Anghttps://zhuanlan.zhihu.com/p/57532328

Deep Learning Fundamentals (Full Stack Deep Learning - Spring 2021)https://www.youtube.com/watch?v=fGxWfEuUu0w&list=PL1T8fO7ArWlcWg04OgNiJy91PywMKT2lv

Deep Learning for Natural Language Processing (Richard Socher, Salesforce)https://www.youtube.com/watch?v=oGk1v1jQITw

Deep Learning for Computer Vision (Andrej Karpathy, OpenAI)https://www.youtube.com/watch?v=u6aEYuemt0MLex Fridman :The talks at the Deep Learning School on September 24/25, 2016 were amazing.Having read, watched, and presented deep learning material o

Question Answering============SQuAD: 100,000+ Questions for Machine Comprehension of Text Bidirectional Attention Flow for Machine Comprehension Reading Wikipedia to Answer Open-Domain Questions Latent Retrieval for Weakly Supervised Open Domain Que

transformer系：transformer-xl，Lite Transformer

Bert系模型：RoBERTa, BART, ALBERT, etc

Question Answering: KB-QARef：KBQA: 趋势浅谈 https://blog.youkuaiyun.com/weixin_43269174/article/details/105464161知识图谱-给AI装个大脑 https://zhuanlan.zhihu.com/knowledgegraph基于知识图谱的问答系统(KBQA) https://blog.youkuaiyun.com/keyue123/article/details/85266355...

Deep Structured Semantic Modelshttps://daiwk.github.io/posts/nlp-dssm.html

NLP for Question Answeringhttps://qa.fastforwardlabs.com/

KG知识图谱

General Language Understanding Evaluation (GLUE) benchmarkRef任务 https://gluebenchmark.com/tasks排行榜 https://gluebenchmark.com/leaderboardGLUE: 自然语言理解的标杆https://blog.youkuaiyun.com/weixin_43269174/article/details/106382651...

ML and NLP Research Highlights of 2020, Sebastian Ruder,https://ruder.io/research-highlights-2020/Scaling up—and down Retrieval augmentation Few-shot learning Contrastive learning Evaluation beyond accuracy Practical concerns of large LMs Multiling

卡耐基梅隆大学计算机学院语言技术系的资源大全（2018更新）【王威廉的链接已失效】1.语言技术系主页  https://lti.cs.cmu.edu/  2.资源大全     http://catalogue.lti.cs.cmu.edu/3. NLP/CL at Carnegie Mellon    http://www.cs.cmu.edu/~nasmith/nlp-cl.html...