NLP-Papers

[TOC]

Papers and Notes

Distributed Representations

• 2017-11
  • 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]
  • Le and Mikolov - 2014 - Distributed representations of sentences and documents [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]
• 2017-12
  • Quick Training of Probabilistic Neural Nets by Importance Sampling [pdf] [pdf(annotated)] [note]

Language Model

• 2017-11
  • Bengio et al. - 2003 - A neural probabilistic language model [pdf]
  • Press and Wolf - 2016 - Using the output embedding to improve language model [pdf]

Machine Translation

• 2017-12
  • 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]
• 2018-05
  • 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]
• 2018-06
  • Gehring et al. - 2017 - Convolutional sequence to sequence learning [pdf]
  • Vaswani et al. - 2017 - Attention is all you need [pdf]

Question Answering

• 2018-03
  • 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]
• 2018-04
  • 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]

Sentences Matching

• 2017-12
  • Hu et al. - 2014 - Convolutional neural network architectures for Matching Natural Language Sentences [pdf] [pdf (annotated)] [note]

Text Classification

• 2017-09

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

• 2017-10

  • 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]

• 2017-11

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

References

• Neural Networks for NLP (CS11-747 Fall 2017 @ CMU)

Deep Learning

• optimization algorithms
  • An overview of gradient descent optimization algorithms

Distributed Representations

• word2vec
  • word2vec(tensorflow)
  • subword-based word vector
• doc2vec
  • doc2vec(gensim)

Language Model

• LSTM for Language Model

Machine Translation

• OpenNMT-py (in PyTorch)
• nmt (in TensorFlow)

Question Answering

• DuReader
• SQuAD
• MS MARCO
• 深度学习解决机器阅读理解任务的研究进展

Text Classification

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