Your Journey to NLP Starts Here !
全面拥抱tensorflow 2.0,代码全部修改为tensorflow 2.0版本。
二. 经典书目(百度云
提取码:txqx)
- 概率图入门.
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- Deep Learning.深度学习必读.
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- Neural Networks and Deep Learning. 入门必读.
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- 斯坦福大学《语音与语言处理》第三版:NLP必读.
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- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
- GPT: Improving Language Understanding by Generative Pre-Training by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
- GPT-2: Language Models are Unsupervised Multitask Learners by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
- Transformer-XL: Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
- XLNet: Generalized Autoregressive Pretraining for Language Understanding by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
- XLM: Cross-lingual Language Model Pretraining by Guillaume Lample and Alexis Conneau.
- RoBERTa: Robustly Optimized BERT Pretraining Approach by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
- DistilBERT: a distilled version of BERT: smaller, faster, cheaper and lighter by Victor Sanh, Lysandre Debut and Thomas Wolf.
- CTRL: A Conditional Transformer Language Model for Controllable Generation by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
- CamemBERT: a Tasty French Language Model by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
- ALBERT: A Lite BERT for Self-supervised Learning of Language Representations, by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
- T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
- XLM-RoBERTa: Unsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
- MMBT: Supervised Multimodal Bitransformers for Classifying Images and Text by Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Davide Testuggine.
- FlauBERT: Unsupervised Language Model Pre-training for French by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
- LSTM(Long Short-term Memory).
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- Sequence to Sequence Learning with Neural Networks.
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- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.
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- Residual Network(Deep Residual Learning for Image Recognition).
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- Dropout(Improving neural networks by preventing co-adaptation of feature detectors).
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- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.
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- An overview of gradient descent optimization algorithms.
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- Analysis Methods in Neural Language Processing: A Survey.
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- Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.
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- A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications.
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- A Gentle Introduction to Deep Learning for Graphs.
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- A Neural Probabilistic Language Model.
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- word2vec Parameter Learning Explained.
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- Language Models are Unsupervised Multitask Learners.
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- An Empirical Study of Smoothing Techniques for Language Modeling.
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- Efficient Estimation of Word Representations in Vector Space.
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- Distributed Representations of Sentences and Documents.
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- Enriching Word Vectors with Subword Information(FastText).
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.解读
- GloVe: Global Vectors for Word Representation.
官网
- ELMo (Deep contextualized word representations).
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- Pre-Training with Whole Word Masking for Chinese BERT.
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- Bag of Tricks for Efficient Text Classification (FastText).
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- Convolutional Neural Networks for Sentence Classification.
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- Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification.
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- A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation.
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- SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient.
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- Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks.
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- Learning Text Similarity with Siamese Recurrent Networks.
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- A Deep Architecture for Matching Short Texts.
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- A Question-Focused Multi-Factor Attention Network for Question Answering.
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- The Design and Implementation of XiaoIce, an Empathetic Social Chatbot.
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- A Knowledge-Grounded Neural Conversation Model.
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- Neural Generative Question Answering.
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- Sequential Matching Network A New Architecture for Multi-turn Response Selection in Retrieval-Based Chatbots.
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- Modeling Multi-turn Conversation with Deep Utterance Aggregation.
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- Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network.
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- Deep Reinforcement Learning For Modeling Chit-Chat Dialog With Discrete Attributes.
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- Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation.
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- Neural Machine Translation by Jointly Learning to Align and Translate.
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- Transformer (Attention Is All You Need).
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- Transformer-XL:Attentive Language Models Beyond a Fixed-Length Context.
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- Get To The Point: Summarization with Pointer-Generator Networks.
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- Deep Recurrent Generative Decoder for Abstractive Text Summarization.
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- Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks.
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- Neural Relation Extraction with Multi-lingual Attention.
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- FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation.
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- End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures.
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- 应聘机器学习工程师?这是你需要知道的12个基础面试问题.
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- 如何学习自然语言处理(综合版).
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- The Illustrated Transformer.
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- Attention-based-model.
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- Modern Deep Learning Techniques Applied to Natural Language Processing.
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- Bert解读.
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- 难以置信!LSTM和GRU的解析从未如此清晰(动图+视频)。
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- 深度学习中优化方法.
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- 从语言模型到Seq2Seq:Transformer如戏,全靠Mask.
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- Applying word2vec to Recommenders and Advertising.
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- 2019 NLP大全:论文、博客、教程、工程进展全梳理.
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- transformers.
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- HanLP.
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