Deep Learning NLP paper Notes

Welcome! The papers are clustered by the problem domain.

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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] 📋

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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]

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Word embeddings

• context2vec: Learning Generic Context Embedding with Bidirectional LSTM [arXiv]

• Distributed Representations of Sentences and Documents [arXiv]