Deep Learning NLP paper Notes

Welcome! The papers are clustered by the problem domain.

seq2seq

Neural Machine Translation(NMT)

Sequence to Sequence Learning with Neural Networks[arXiv] [notes] 📋
Neural Machine Translation by Jointly Learning to Align and Translate (2015)[arXiv] [notes] 📋
Effective Approaches to Attention-based Neural Machine Translation [arXiv] [notes] 📋
Massive Exploration of Neural Machine Translation Architectures[arXiv] [notes] 📋

Graph embeddings

DeepWalk: Online Learning of Social Representations, KDD'14 [arXiv]
node2vec: Scalable Feature Learning for Networks, KDD'16 [arXiv]
Structural Deep Network Embedding [arXiv]
Semi-Supervised Classification with Graph Convolutional Networks [arXiv]
Asymmetric Transitivity Preserving Graph Embedding or High-Order Proximity preserved Embedding(HOPE for short) [arXiv]
LINE: Large-scale information network embedding, WWW'15 [arXiv]
Grarep: Learning graph representations with global structural information [arXiv]

Word embeddings

context2vec: Learning Generic Context Embedding with Bidirectional LSTM [arXiv]
Distributed Representations of Sentences and Documents [arXiv]