1. Distributed Representations of Sentences and Documents
2. GloVe: Global Vectors for Word Representation
3. Skip-Thought Vectors
4. Convolutional Neural Networks for Sentence Classification
5. Character-level Convolutional Networks for Text Classification
6. Hierarchical Attention Networks for Document Classification
7. Neural Relation Extraction with Selective Attention over Instances
8. End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
9. Sequence to Sequence Learning with Neural Networks
10. Convolutional Sequence to Sequence Learning
11. Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
12. Phrase-Based & Neural Unsupervised Machine Translation
13. Get To The Point: Summarization with Pointer-Generator Networks
14. End-To-End Memory Networks
15. QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
16. Bidirectional Attention Flow for Machine Comprehension
17. Adversarial Learning for Neural Dialogue Generation
18. SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
19. Modeling Relational Data with Graph Convolutional Networks
20. Exploring the Limits of Language Modeling
21. Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
22. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
23. Deep contextualized word representations
24. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
25. XLNet: Generalized Autoregressive Pretraining for Language Understanding
26. Efficient Estimation of Word Representations in Vector Space
27. Neural Machine Translation by Jointly Learning to Align and Translate
28. Attention is all you need
29. A Convolutional Neural Network for Modelling Sentences
30. Bag of Tricks for Efficient Text Classification
31. Siamese recurrent architectures for learning sentence similarity