• Faruqui and Dyer - 2014 - Improving vector space word representations using multilingual correlation [pdf] [note]
• Maaten and Hinton - 2008 - Visualizing data using t-SNE [pdf] [pdf (annotated)] [note]
• Ling et al. - 2015 - Finding function in form: Compositional character models for open vocabulary word representation [pdf] [pdf (annotated)] [note]
• Bojanowski et al. - 2016 - Enriching word vectors with subword information [pdf] [pdf (annotated)] [note]

• Bengio and Senécal - 2003 - Quick Training of Probabilistic Neural Nets by Importance Sampling [pdf] [pdf(annotated)] [note]

• word2vec(tensorflow)
• subword-based word vector
• Chinese Word Vectors 中文词向量
• Tencent AI Lab Embedding Corpus for Over 8 Million Chinese Words and Phrases

• Le and Mikolov - 2014 - Distributed representations of sentences and documents [pdf] [pdf (annotated)] [note]

• Li and Hovy - 2014 - A Model of Coherence Based on Distributed Sentence Representation [pdf] [pdf (annotated)] [note]
• Kiros et al. - 2015 - Skip-Thought Vectors [pdf] [pdf (annotated)] [note]
• Hill et al. - 2016 - Learning Distributed Representations of Sentences from Unlabelled Data [pdf] [pdf (annotated)] [note]
• Arora et al. - 2016 - A simple but tough-to-beat baseline for sentence embeddings [pdf] [pdf (annotated)] [note]
• Pagliardini et al. - 2017 - Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features (sent2vec) [pdf] [pdf (annotated)] [note]
• Logeswaran et al. - 2018 - An efficient framework for learning sentence representations (Quick-Thought Vectors) [pdf] [pdf (annotated)] [note]

• Wieting et al. - 2015 - Towards universal paraphrastic sentence embeddings [pdf] [pdf (annotated)] [note]
• Adi et al. - 2016 - Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks [pdf] [pdf (annotated)] [note]
• Conneau et al. - 2017 - Supervised Learning of Universal Sentence Representations from Natural Language Inference Data (InferSent) [pdf] [pdf (annotated)] [note]
• Cer et al. - 2018 - Universal Sentence Encoder [pdf] [pdf (annotated)] [note]

• awesome-sentence-embedding: A curated list of pretrained sentence(and word) embedding models
• SentEval: evaluation toolkit for sentence embeddings
• doc2vec(gensim)
• Skip-Thought Vectors
• SIF(sentence embedding by Smooth Inverse Frequency weighting scheme)
• Quick-Thought Vectors
• sent2vec
• InferSent

• Lample et al. - 2016 - Neural Architectures for Named Entity Recognition [pdf]
• Ma and Hovy - 2016 - End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF [pdf]
• Yang et al. - 2017 - Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks [pdf]
• Peters et al. - 2017 - Semi-supervised sequence tagging with bidirectional language models [pdf]
• Shang et al. - 2018 - Learning Named Entity Tagger using Domain-Specific Dictionary [pdf]

• ChineseNER (TensorFlow)
• flair (PyTorch)

• Bengio et al. - 2003 - A neural probabilistic language model [pdf]
• Press and Wolf - 2016 - Using the output embedding to improve language model [pdf]

• Peters et al. - 2018- Deep contextualized word representations(ELMo) [pdf] [note]
• Howard and Ruder - 2018 - Universal language model fine-tuning for text classification(ULMFit) [pdf]
• Radford et al. - 2018 - Improving language understanding by generative pre-training [pdf]
• Devlin et al. - 2018 - Bert: Pre-training of deep bidirectional transformers for language understanding [pdf]

• Blog:The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)
• ELMo
  • ELMo(AllenNLP)
  • Pre-trained ELMo Representations for Many Languages
• Quick Start: Training an IMDb sentiment model with ULMFiT
• finetune-transformer-lm: Code and model for the paper "Improving Language Understanding by Generative Pre-Training"
• awesome-bert: bert nlp papers, applications and github resources ， BERT 相关论文和 github 项目

• Oda et al. - 2017 - Neural Machine Translation via Binary Code Predict [pdf] [note]
• Kalchbrenner et al. - 2016 - Neural machine translation in linear time [pdf] [pdf (annotated)] [note]

• Sutskever et al. - 2014 - Sequence to Sequence Learning with Neural Networks [pdf]
• Cho et al. - 2014 - Learning Phrase Representations using RNN Encoder-Decoder for NMT [pdf]
• Bahdanau et al. - 2014 - NMT by Jointly Learning to Align and Translate [pdf]
• Luong et al. - 2015 - Effective Approaches to Attention-based NMT [pdf]

• Gehring et al. - 2017 - Convolutional sequence to sequence learning [pdf]
• Vaswani et al. - 2017 - Attention is all you need [pdf] [note1:The Illustrated Transformer] [note2:The Annotated Transformer]

• OpenNMT-py (in PyTorch)
• nmt (in TensorFlow)
• MT-Reading-List

• Wang and Jiang. - 2016 - Machine Comprehension Using Match-LSTM and Answer Pointer [pdf]
• Seo et al. - 2016 - Bidirectional Attention Flow for Machine Comprehension [pdf]
• Cui et al. - 2016 - Attention-over-Attention Neural Networks for Reading Comprehension [pdf]

• Clark and Gardner. - 2017 - Simple and Effective Multi-Paragraph Reading Comprehension [pdf]
• Wang et al. - 2017 - Gated Self-Matching Networks for Reading Comprehension and Question Answering [pdf]
• Yu et al. - 2018 - QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension [pdf]

• DuReader
• SQuAD
• MS MARCO
• 深度学习解决机器阅读理解任务的研究进展
• RCPapers: Must-read papers on Machine Reading Comprehension

• Mintz et al. - 2009 - Distant supervision for relation extraction without labeled data [pdf]
• Zeng et al. - 2015 - Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks [pdf]
• Zhou et al. - 2016 - Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification [pdf]
• Lin et al. - 2016 - Neural Relation Extraction with Selective Attention over Instances [pdf]

• Ji et al. - 2017 - Distant Supervision for Relation Extraction with Sentence-Level Attention and Entity Descriptions [pdf]
• Levy et al. - 2017 - Zero-Shot Relation Extraction via Reading Comprehension [pdf]

• OpenNRE
• NREPapers: Must-read papers on neural relation extraction (NRE)
• awesome-relation-extraction

• Hu et al. - 2014 - Convolutional neural network architectures for Matching Natural Language Sentences [pdf] [pdf (annotated)] [note]

• Nie and Bansal - 2017 - Shortcut-Stacked Sentence Encoders for Multi-Domain Inference [pdf] [note]
• Wang et al. - 2017 - Bilateral Multi-Perspective Matching for Natural Language Sentences [pdf] [note]
• Tay et al. - 2017 - A Compare-Propagate Architecture with Alignment Factorization for Natural Language Inference [pdf]
• Chen et al. - 2017 - Enhanced LSTM for Natural Language Inference [pdf] [note]
• Ghaeini et al. - 2018 - DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference [pdf]

• The Stanford Natural Language Inference (SNLI) Corpus

• Joulin et al. - 2016 - Bag of tricks for efficient text classification [pdf] [pdf (annotated)] [note]

• Kim - 2014 - Convolutional neural networks for sentence classification [pdf] [pdf (annotated)] [note]
• Zhang and Wallace - 2015 - A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification [pdf] [pdf (annotated)] [note]
• Zhang et al. - 2015 - Character-level convolutional networks for text classification [pdf] [pdf (annotated)] [note]
• Lai et al. - 2015 - Recurrent Convolutional Neural Networks for Text Classification [pdf] [pdf (annotated)] [note]

• Iyyer et al. - 2015 - Deep unordered composition rivals syntactic methods for Text Classification [pdf] [pdf (annotated)] [note]

• fastText
• text_classification
• PyTorchText(知乎看山杯)

Neural Networks for NLP (CS11-747 Spring 2019 @ CMU)

optimization algorithms

• An overview of gradient descent optimization algorithms

NLP-progress

Awesome-Chinese-NLP

StateOfTheArt.ai

funNLP(从文本中抽取结构化信息等资源汇总)

StanfordNLP: Official Stanford NLP Python Library for Many Human Languages

Browse state-of-the-art