gdtop818

Natural Language Processing[论文合集]

https://handong1587.github.io/deep_learning/2015/10/09/nlp.html

Jump to...

1. Tutorials

2. Neural Models

3. Sequence to Sequence Learning

4. Translation

5. Summarization

6. Reading Comprehension

7. Language Understanding

8. Text Classification

9. Text Clustering

10. Alignment

11. Dialog

12. Memory Networks

13. Papers

1. Interesting Applications

14. Project

15. Datasets

16. Blogs

1. Word2Vec

17. Demos

18. Talks / Videos

19. Resources

Tutorials

Practical Neural Networks for NLP

intro: EMNLP 2016
github: https://github.com/clab/dynet_tutorial_examples

Structured Neural Networks for NLP: From Idea to Code

slides: https://github.com/neubig/yrsnlp-2016/blob/master/neubig16yrsnlp.pdf
github: https://github.com/neubig/yrsnlp-2016

Understanding Deep Learning Models in NLP

http://nlp.yvespeirsman.be/blog/understanding-deeplearning-models-nlp/

Deep learning for natural language processing, Part 1

https://softwaremill.com/deep-learning-for-nlp/

Neural Models

Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models

intro: NIPS 2014 deep learning workshop
arxiv: http://arxiv.org/abs/1411.2539
github: https://github.com/ryankiros/visual-semantic-embedding
results: http://www.cs.toronto.edu/~rkiros/lstm_scnlm.html
demo: http://deeplearning.cs.toronto.edu/i2t

Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks

arxiv: http://arxiv.org/abs/1503.00075
github: https://github.com/stanfordnlp/treelstm
github(Theano): https://github.com/ofirnachum/tree_rnn

Visualizing and Understanding Neural Models in NLP

arxiv: http://arxiv.org/abs/1506.01066
github: https://github.com/jiweil/Visualizing-and-Understanding-Neural-Models-in-NLP

Character-Aware Neural Language Models

paper: http://arxiv.org/abs/1508.06615
github: https://github.com/yoonkim/lstm-char-cnn

Skip-Thought Vectors

paper: http://arxiv.org/abs/1506.06726
github: https://github.com/ryankiros/skip-thoughts

A Primer on Neural Network Models for Natural Language Processing

arxiv: http://arxiv.org/abs/1510.00726

Character-aware Neural Language Models

arxiv: http://arxiv.org/abs/1508.06615

Neural Variational Inference for Text Processing

arxiv: http://arxiv.org/abs/1511.06038
notes: http://dustintran.com/blog/neural-variational-inference-for-text-processing/
github: https://github.com/carpedm20/variational-text-tensorflow
github: https://github.com/cheng6076/NVDM

Sequence to Sequence Learning

Generating Text with Deep Reinforcement Learning

intro: NIPS 2015
arxiv: http://arxiv.org/abs/1510.09202

MUSIO: A Deep Learning based Chatbot Getting Smarter

homepage: http://ec2-204-236-149-143.us-west-1.compute.amazonaws.com:9000/
github(Torch7): https://github.com/deepcoord/seq2seq

Translation

Learning phrase representations using rnn encoder-decoder for statistical machine translation

intro: GRU. EMNLP 2014
arxiv: http://arxiv.org/abs/1406.1078

Neural Machine Translation by Jointly Learning to Align and Translate

intro: ICLR 2015
arxiv: http://arxiv.org/abs/1409.0473
github: https://github.com/lisa-groundhog/GroundHog

Multi-Source Neural Translation

intro: “report up to +4.8 Bleu increases on top of a very strong attention-based neural translation model.”
arxiv: Multi-Source Neural Translation
github(Zoph_RNN): https://github.com/isi-nlp/Zoph_RNN
video: http://research.microsoft.com/apps/video/default.aspx?id=260336

Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism

arxiv: http://arxiv.org/abs/1601.01073
github: https://github.com/nyu-dl/dl4mt-multi
notes: https://github.com/dennybritz/deeplearning-papernotes/blob/master/notes/multi-way-nmt-shared-attention.md

Modeling Coverage for Neural Machine Translation

arxiv: http://arxiv.org/abs/1601.04811

A Character-level Decoder without Explicit Segmentation for Neural Machine Translation

arxiv: http://arxiv.org/abs/1603.06147
github: https://github.com/nyu-dl/dl4mt-cdec

NEMATUS: Attention-based encoder-decoder model for neural machine translation

github: https://github.com/rsennrich/nematus

Variational Neural Machine Translation

intro: EMNLP 2016
arxiv: https://arxiv.org/abs/1605.07869
github: https://github.com/DeepLearnXMU/VNMT

Neural Network Translation Models for Grammatical Error Correction

arxiv: http://arxiv.org/abs/1606.00189

Linguistic Input Features Improve Neural Machine Translation

arxiv: http://arxiv.org/abs/1606.02892
github: https://github.com/rsennrich/nematus

Sequence-Level Knowledge Distillation

intro: EMNLP 2016
arxiv: http://arxiv.org/abs/1606.07947
github: https://github.com/harvardnlp/nmt-android

Neural Machine Translation: Breaking the Performance Plateau

slides: http://www.meta-net.eu/events/meta-forum-2016/slides/09_sennrich.pdf

Tips on Building Neural Machine Translation Systems

github: https://github.com/neubig/nmt-tips

Semi-Supervised Learning for Neural Machine Translation

intro: ACL 2016. Tsinghua University & Baidu Inc
arxiv: http://arxiv.org/abs/1606.04596

EUREKA-MangoNMT: A C++ toolkit for neural machine translation for CPU

github: https://github.com/jiajunzhangnlp/EUREKA-MangoNMT

Deep Character-Level Neural Machine Translation

github: https://github.com/SwordYork/DCNMT

Neural Machine Translation Implementations

github: https://github.com/jonsafari/nmt-list

Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

arxiv: http://arxiv.org/abs/1609.08144v1

Learning to Translate in Real-time with Neural Machine Translation

arxiv: https://arxiv.org/abs/1610.00388

Is Neural Machine Translation Ready for Deployment? A Case Study on 30 Translation Directions

arxiv: https://arxiv.org/abs/1610.01108
github: https://github.com/emjotde/amunmt

Fully Character-Level Neural Machine Translation without Explicit Segmentation

arxiv: https://arxiv.org/abs/1610.03017
github: https://github.com/nyu-dl/dl4mt-c2c

Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation

arxiv: https://arxiv.org/abs/1610.03164

Neural Machine Translation in Linear Time

intro: ByteNet
arxiv: https://arxiv.org/abs/1610.10099
github: https://github.com/paarthneekhara/byteNet-tensorflow
github(Tensorflow): https://github.com/buriburisuri/ByteNet

Neural Machine Translation with Reconstruction

arxiv: https://arxiv.org/abs/1611.01874

A Convolutional Encoder Model for Neural Machine Translation

intro: ACL 2017. Facebook AI Research
arxiv: https://arxiv.org/abs/1611.02344
github: https://github.com//pravarmahajan/cnn-encoder-nmt

Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder

arxiv: https://arxiv.org/abs/1611.04798

MXNMT: MXNet based Neural Machine Translation

github: https://github.com/magic282/MXNMT

Doubly-Attentive Decoder for Multi-modal Neural Machine Translation

intro: Dublin City University & Trinity College Dublin
arxiv: https://arxiv.org/abs/1702.01287

Massive Exploration of Neural Machine Translation Architectures

intro: Google Brain
arxiv: https://arxiv.org/abs/1703.03906
github: https://github.com/google/seq2seq/

Depthwise Separable Convolutions for Neural Machine Translation

intro: Google Brain & University of Toronto
arxiv: https://arxiv.org/abs/1706.03059

Deep Architectures for Neural Machine Translation

intro: WMT 2017 research track. University of Edinburgh & Charles University
arxiv: https://arxiv.org/abs/1707.07631
github: https://github.com/Avmb/deep-nmt-architectures

Marian: Fast Neural Machine Translation in C++

intro: Microsoft & Adam Mickiewicz University in Poznan & University of Edinburgh
homepage: https://marian-nmt.github.io/
arxiv: https://arxiv.org/abs/1804.00344
github: https://github.com/marian-nmt/marian

Sockeye

intro: Sequence-to-sequence framework with a focus on Neural Machine Translation based on Apache MXNet
arxiv: https://github.com/awslabs/sockeye/

Summarization

Extraction of Salient Sentences from Labelled Documents

arxiv: http://arxiv.org/abs/1412.6815
github: https://github.com/mdenil/txtnets
notes: https://github.com/jxieeducation/DIY-Data-Science/blob/master/papernotes/2014/06/model-visualizing-summarising-conv-net.md

A Neural Attention Model for Abstractive Sentence Summarization

intro: EMNLP 2015. Facebook AI Research
arxiv: http://arxiv.org/abs/1509.00685
github: https://github.com/facebook/NAMAS
github(TensorFlow): https://github.com/carpedm20/neural-summary-tensorflow

A Convolutional Attention Network for Extreme Summarization of Source Code

homepage: http://groups.inf.ed.ac.uk/cup/codeattention/
arxiv: http://arxiv.org/abs/1602.03001
github: https://github.com/jxieeducation/DIY-Data-Science/blob/master/papernotes/2016/02/conv-attention-network-source-code-summarization.md

Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond

intro: BM Watson & Université de Montréal
arxiv: http://arxiv.org/abs/1602.06023

textsum: Text summarization with TensorFlow

blog: https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html
github: https://github.com/tensorflow/models/tree/master/textsum

How to Run Text Summarization with TensorFlow

blog: https://medium.com/@surmenok/how-to-run-text-summarization-with-tensorflow-d4472587602d#.mll1rqgjg
github: https://github.com/surmenok/TextSum

Reading Comprehension

Text Comprehension with the Attention Sum Reader Network

Text Understanding with the Attention Sum Reader Network

intro: ACL 2016
arxiv: https://arxiv.org/abs/1603.01547
github: https://github.com/rkadlec/asreader

A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task

arxiv: http://arxiv.org/abs/1606.02858
github: https://github.com/danqi/rc-cnn-dailymail

Consensus Attention-based Neural Networks for Chinese Reading Comprehension

arxiv: http://arxiv.org/abs/1607.02250
dataset(“HFL-RC”): http://hfl.iflytek.com/chinese-rc/

Separating Answers from Queries for Neural Reading Comprehension

arxiv: http://arxiv.org/abs/1607.03316
github: https://github.com/dirkweissenborn/qa_network

Attention-over-Attention Neural Networks for Reading Comprehension

arxiv: http://arxiv.org/abs/1607.04423
github: https://github.com/OlavHN/attention-over-attention

Teaching Machines to Read and Comprehend CNN News and Children Books using Torch

github: https://github.com/ganeshjawahar/torch-teacher

Reasoning with Memory Augmented Neural Networks for Language Comprehension

arxiv: https://arxiv.org/abs/1610.06454

Bidirectional Attention Flow: Bidirectional Attention Flow for Machine Comprehension

project page: https://allenai.github.io/bi-att-flow/
github: https://github.com/allenai/bi-att-flow

NewsQA: A Machine Comprehension Dataset

arxiv: https://arxiv.org/abs/1611.09830
dataset: http://datasets.maluuba.com/NewsQA
github: https://github.com/Maluuba/newsqa

Gated-Attention Readers for Text Comprehension

intro: CMU
arxiv: https://arxiv.org/abs/1606.01549
github: https://github.com/bdhingra/ga-reader

Get To The Point: Summarization with Pointer-Generator Networks

intro: ACL 2017. Stanford University & Google Brain
arxiv: https://arxiv.org/abs/1704.04368
github: https://github.com/abisee/pointer-generator

Language Understanding

Recurrent Neural Networks with External Memory for Language Understanding

arxiv: http://arxiv.org/abs/1506.00195
github: https://github.com/npow/RNN-EM

Neural Semantic Encoders

intro: EACL 2017
arxiv: https://arxiv.org/abs/1607.04315
github(Keras): https://github.com/pdasigi/neural-semantic-encoders

Neural Tree Indexers for Text Understanding

arxiv: https://arxiv.org/abs/1607.04492
bitbucket: https://bitbucket.org/tsendeemts/nti/src

Better Text Understanding Through Image-To-Text Transfer

intro: Google Brain & Technische Universität München
arxiv: https://arxiv.org/abs/1705.08386

Text Classification

Convolutional Neural Networks for Sentence Classification

intro: EMNLP 2014
arxiv: http://arxiv.org/abs/1408.5882
github(Theano): https://github.com/yoonkim/CNN_sentence
github(Torch): https://github.com/harvardnlp/sent-conv-torch
github(Keras): https://github.com/alexander-rakhlin/CNN-for-Sentence-Classification-in-Keras
github(Tensorflow): https://github.com/abhaikollara/CNN-Sentence-Classification

Recurrent Convolutional Neural Networks for Text Classification

paper: http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9745/9552
github: https://github.com/knok/rcnn-text-classification

Character-level Convolutional Networks for Text Classification

intro: NIPS 2015. “Text Understanding from Scratch”
arxiv: http://arxiv.org/abs/1509.01626
github: https://github.com/zhangxiangxiao/Crepe
datasets: http://goo.gl/JyCnZq
github(TensorFlow): https://github.com/mhjabreel/CharCNN

A C-LSTM Neural Network for Text Classification

arxiv: http://arxiv.org/abs/1511.08630

Rationale-Augmented Convolutional Neural Networks for Text Classification

arxiv: http://arxiv.org/abs/1605.04469

Text classification using DIGITS and Torch7

github: https://github.com/NVIDIA/DIGITS/tree/master/examples/text-classification

Recurrent Neural Network for Text Classification with Multi-Task Learning

arxiv: http://arxiv.org/abs/1605.05101

Deep Multi-Task Learning with Shared Memory

intro: EMNLP 2016
arxiv: https://arxiv.org/abs/1609.07222

Virtual Adversarial Training for Semi-Supervised Text Classification

Adversarial Training Methods for Semi-Supervised Text Classification

arxiv: http://arxiv.org/abs/1605.07725
notes: https://github.com/dennybritz/deeplearning-papernotes/blob/master/notes/adversarial-text-classification.md

Sentence Convolution Code in Torch: Text classification using a convolutional neural network

github: https://github.com/harvardnlp/sent-conv-torch

Bag of Tricks for Efficient Text Classification

intro: Facebook AI Research
arxiv: http://arxiv.org/abs/1607.01759
github: https://github.com/kemaswill/fasttext_torch
github: https://github.com/facebookresearch/fastText

Actionable and Political Text Classification using Word Embeddings and LSTM

arxiv: http://arxiv.org/abs/1607.02501

Implementing a CNN for Text Classification in TensorFlow

blog: http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/

fancy-cnn: Multiparadigm Sequential Convolutional Neural Networks for text classification

github: https://github.com/textclf/fancy-cnn

Convolutional Neural Networks for Text Categorization: Shallow Word-level vs. Deep Character-level

arxiv: http://arxiv.org/abs/1609.00718

Tweet Classification using RNN and CNN

github: https://github.com/ganeshjawahar/tweet-classify

Hierarchical Attention Networks for Document Classification

intro: CMU & MSR. NAACL 2016
paper: https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf
github(TensorFlow): https://github.com/raviqqe/tensorflow-font2char2word2sent2doc
github(TensorFlow): https://github.com/ematvey/deep-text-classifier

AC-BLSTM: Asymmetric Convolutional Bidirectional LSTM Networks for Text Classification

arxiv: https://arxiv.org/abs/1611.01884
github(MXNet): https://github.com/Ldpe2G/AC-BLSTM

Generative and Discriminative Text Classification with Recurrent Neural Networks

intro: DeepMind
arxiv: https://arxiv.org/abs/1703.01898

Adversarial Multi-task Learning for Text Classification

intro: ACL 2017
arxiv: https://arxiv.org/abs/1704.05742
data: http://nlp.fudan.edu.cn/data/

Deep Text Classification Can be Fooled

intro: Renmin University of China
arxiv: https://arxiv.org/abs/1704.08006

Deep neural network framework for multi-label text classification

github: https://github.com/inspirehep/magpie

Multi-Task Label Embedding for Text Classification

intro: Shanghai Jiao Tong University
arxiv: https://arxiv.org/abs/1710.07210

Text Clustering

Self-Taught Convolutional Neural Networks for Short Text Clustering

intro: Chinese Academy of Sciences. accepted for publication in Neural Networks
arxiv: https://arxiv.org/abs/1701.00185
github: https://github.com/jacoxu/STC2

Alignment

Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books

arxiv: http://arxiv.org/abs/1506.06724
github: https://github.com/ryankiros/neural-storyteller

Dialog

Visual Dialog

webiste: http://visualdialog.org/
arxiv: https://arxiv.org/abs/1611.08669
github: https://github.com/batra-mlp-lab/visdial-amt-chat
github(Torch): https://github.com/batra-mlp-lab/visdial
github(PyTorch): https://github.com/Cloud-CV/visual-chatbot
demo: http://visualchatbot.cloudcv.org/

Papers, code and data from FAIR for various memory-augmented nets with application to text understanding and dialogue.

post: https://www.facebook.com/yann.lecun/posts/10154070851697143

Neural Emoji Recommendation in Dialogue Systems

intro: Tsinghua University & Baidu
arxiv: https://arxiv.org/abs/1612.04609

Memory Networks

Neural Turing Machines

paper: http://arxiv.org/abs/1410.5401
Chs: http://www.jianshu.com/p/94dabe29a43b
github: https://github.com/shawntan/neural-turing-machines
github: https://github.com/DoctorTeeth/diffmem
github: https://github.com/carpedm20/NTM-tensorflow
blog: https://blog.aidangomez.ca/2016/05/16/The-Neural-Turing-Machine/

Memory Networks

intro: Facebook AI Research
arxiv: http://arxiv.org/abs/1410.3916
github: https://github.com/npow/MemNN

End-To-End Memory Networks

intro: Facebook AI Research
intro: Continuous version of memory extraction via softmax. “Weakly supervised memory networks”
arxiv: http://arxiv.org/abs/1503.08895
github: https://github.com/facebook/MemNN
github: https://github.com/vinhkhuc/MemN2N-babi-python
github: https://github.com/npow/MemN2N
github: https://github.com/domluna/memn2n
github(Tensorflow): https://github.com/abhaikollara/MemN2N-Tensorflow
video: http://research.microsoft.com/apps/video/default.aspx?id=259920&r=1
video: http://pan.baidu.com/s/1pKiGLzP

Reinforcement Learning Neural Turing Machines - Revised

arxiv: http://arxiv.org/abs/1505.00521
github: https://github.com/ilyasu123/rlntm

Learning to Transduce with Unbounded Memory

intro: Google DeepMind
arxiv: http://arxiv.org/abs/1506.02516

How to Code and Understand DeepMind’s Neural Stack Machine

blog: https://iamtrask.github.io/2016/02/25/deepminds-neural-stack-machine/
video tutorial: http://pan.baidu.com/s/1qX0EGDe

Ask Me Anything: Dynamic Memory Networks for Natural Language Processing

intro: Memory networks implemented via rnns and gated recurrent units (GRUs).
arxiv: http://arxiv.org/abs/1506.07285
blog(“Implementing Dynamic memory networks”): http://yerevann.github.io//2016/02/05/implementing-dynamic-memory-networks/
github(Python): https://github.com/swstarlab/DynamicMemoryNetworks

Ask Me Even More: Dynamic Memory Tensor Networks (Extended Model)

intro: extensions for the Dynamic Memory Network (DMN)
arxiv: https://arxiv.org/abs/1703.03939
github: https://github.com/rgsachin/DMTN

Structured Memory for Neural Turing Machines

intro: IBM Watson
arxiv: http://arxiv.org/abs/1510.03931

Dynamic Memory Networks for Visual and Textual Question Answering

intro: MetaMind 2016
arxiv: http://arxiv.org/abs/1603.01417
slides: http://slides.com/smerity/dmn-for-tqa-and-vqa-nvidia-gtc#/
github: https://github.com/therne/dmn-tensorflow
github(Theano): https://github.com/ethancaballero/Improved-Dynamic-Memory-Networks-DMN-plus
review: https://www.technologyreview.com/s/600958/the-memory-trick-making-computers-seem-smarter/
github(Tensorflow): https://github.com/DeepRNN/visual_question_answering

Neural GPUs Learn Algorithms

arxiv: http://arxiv.org/abs/1511.08228
github: https://github.com/tensorflow/models/tree/master/neural_gpu
github: https://github.com/ikostrikov/torch-neural-gpu
github: https://github.com/tristandeleu/neural-gpu

Hierarchical Memory Networks

arxiv: http://arxiv.org/abs/1605.07427

Convolutional Residual Memory Networks

arxiv: http://arxiv.org/abs/1606.05262

NTM-Lasagne: A Library for Neural Turing Machines in Lasagne

github: https://github.com/snipsco/ntm-lasagne
blog: https://medium.com/snips-ai/ntm-lasagne-a-library-for-neural-turing-machines-in-lasagne-2cdce6837315#.96pnh1m6j

Evolving Neural Turing Machines for Reward-based Learning

homepage: http://sebastianrisi.com/entm/
paper: http://sebastianrisi.com/wp-content/uploads/greve_gecco16.pdf
code: https://www.dropbox.com/s/t019mwabw5nsnxf/neuralturingmachines-master.zip?dl=0

Hierarchical Memory Networks for Answer Selection on Unknown Words

intro: COLING 2016
arxiv: https://arxiv.org/abs/1609.08843
github: https://github.com/jacoxu/HMN4QA

Gated End-to-End Memory Networks

arxiv: https://arxiv.org/abs/1610.04211

Can Active Memory Replace Attention?

intro: Google Brain
arxiv: https://arxiv.org/abs/1610.08613

A Taxonomy for Neural Memory Networks

intro: University of Florida
arxiv: https://arxiv.org/abs/1805.00327

Papers

Globally Normalized Transition-Based Neural Networks

intro: speech tagging, dependency parsing and sentence compression
arxiv: http://arxiv.org/abs/1603.06042
github(SyntaxNet): https://github.com/tensorflow/models/tree/master/syntaxnet

A Decomposable Attention Model for Natural Language Inference

intro: EMNLP 2016
arxiv: http://arxiv.org/abs/1606.01933
github(Keras+spaCy): https://github.com/explosion/spaCy/tree/master/examples/keras_parikh_entailment

Improving Recurrent Neural Networks For Sequence Labelling

arxiv: http://arxiv.org/abs/1606.02555

Recurrent Memory Networks for Language Modeling

arixv: http://arxiv.org/abs/1601.01272
github: https://github.com/ketranm/RMN

Tweet2Vec: Learning Tweet Embeddings Using Character-level CNN-LSTM Encoder-Decoder

intro: MIT Media Lab
arixv: http://arxiv.org/abs/1607.07514

Learning text representation using recurrent convolutional neural network with highway layers

intro: Neu-IR ‘16 SIGIR Workshop on Neural Information Retrieval
arxiv: http://arxiv.org/abs/1606.06905
github: https://github.com/wenying45/deep_learning_tutorial/tree/master/rcnn-hw

Ask the GRU: Multi-task Learning for Deep Text Recommendations

arxiv: http://arxiv.org/abs/1609.02116

From phonemes to images: levels of representation in a recurrent neural model of visually-grounded language learning

intro: COLING 2016
arxiv: https://arxiv.org/abs/1610.03342

Visualizing Linguistic Shift

arxiv: https://arxiv.org/abs/1611.06478

A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks

intro: The University of Tokyo & Salesforce Research
arxiv: https://arxiv.org/abs/1611.01587

Deep Learning applied to NLP

https://arxiv.org/abs/1703.03091

Attention Is All You Need

intro: Google Brain & Google Research & University of Toronto
intro: Just attention + positional encoding = state of the art
arxiv: https://arxiv.org/abs/1706.03762
github(Chainer): https://github.com/soskek/attention_is_all_you_need

Recent Trends in Deep Learning Based Natural Language Processing

intro: Beijing Institute of Technology & National University of Singapore & Nanyang Technological University
arxiv: https://arxiv.org/abs/1708.02709

HotFlip: White-Box Adversarial Examples for NLP

intro: University of Oregon & Nanjing University
arxiv: https://arxiv.org/abs/1712.06751

No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling

intro: ACL 2018
arxiv: https://arxiv.org/abs/1804.09160

Interesting Applications

Data-driven HR - Résumé Analysis Based on Natural Language Processing and Machine Learning

arxiv: http://arxiv.org/abs/1606.05611

sk_p: a neural program corrector for MOOCs

intro: MIT
intro: Using seq2seq to fix buggy code submissions in MOOCs
arxiv: http://arxiv.org/abs/1607.02902

Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge

intro: EMNLP 2016
intro: translating natural language queries into regular expressions which embody their meaning
arxiv: http://arxiv.org/abs/1608.03000

emoji2vec: Learning Emoji Representations from their Description

intro: EMNLP 2016
arxiv: http://arxiv.org/abs/1609.08359

Inside-Outside and Forward-Backward Algorithms Are Just Backprop (Tutorial Paper)

paper: https://www.cs.jhu.edu/~jason/papers/eisner.spnlp16.pdf

Cruciform: Solving Crosswords with Natural Language Processing

arxiv: https://arxiv.org/abs/1611.02360

Smart Reply: Automated Response Suggestion for Email

intro: Google. KDD 2016
arxiv: https://arxiv.org/abs/1606.04870
notes: https://blog.acolyer.org/2016/11/24/smart-reply-automated-response-suggestion-for-email/

Deep Learning for RegEx

intro: a winning submission of Extraction of product attribute values competition (CrowdAnalytix)
blog: http://dlacombejr.github.io/2016/11/13/deep-learning-for-regex.html

Learning Python Code Suggestion with a Sparse Pointer Network

intro: Learning to Auto-Complete using RNN Language Models
intro: University College London
arxiv: https://arxiv.org/abs/1611.08307
github: https://github.com/uclmr/pycodesuggest

End-to-End Prediction of Buffer Overruns from Raw Source Code via Neural Memory Networks

https://arxiv.org/abs/1703.02458

Convolutional Sequence to Sequence Learning

arxiv: https://arxiv.org/abs/1705.03122
paper: https://s3.amazonaws.com/fairseq/papers/convolutional-sequence-to-sequence-learning.pdf
github: https://github.com/facebookresearch/fairseq

DeepFix: Fixing Common C Language Errors by Deep Learning

intro: AAAI 2017. Indian Institute of Science
project page: http://www.iisc-seal.net/deepfix
paper: https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14603/13921
bitbucket: https://bitbucket.org/iiscseal/deepfix

Hierarchically-Attentive RNN for Album Summarization and Storytelling

intro: EMNLP 2017. UNC Chapel Hill
arxiv: https://arxiv.org/abs/1708.02977

Project

TheanoLM - An Extensible Toolkit for Neural Network Language Modeling

arxiv: http://arxiv.org/abs/1605.00942
github: https://github.com/senarvi/theanolm

NLP-Caffe: natural language processing with Caffe

github: https://github.com/Russell91/nlpcaffe

DL4NLP: Deep Learning for Natural Language Processing

github: https://github.com/nokuno/dl4nlp

Combining CNN and RNN for spoken language identification

blog: http://yerevann.github.io//2016/06/26/combining-cnn-and-rnn-for-spoken-language-identification/
github: https://github.com/YerevaNN/Spoken-language-identification/tree/master/theano

Character-Aware Neural Language Models: LSTM language model with CNN over characters in TensorFlow

github: https://github.com/carpedm20/lstm-char-cnn-tensorflow

Neural Relation Extraction with Selective Attention over Instances

paper: http://nlp.csai.tsinghua.edu.cn/~lzy/publications/acl2016_nre.pdf
github: https://github.com/thunlp/NRE

deep-simplification: Text simplification using RNNs

intro: achieves a BLEU score of 61.14
github: https://github.com/mbartoli/deep-simplification

lamtram: A toolkit for language and translation modeling using neural networks

github: https://github.com/neubig/lamtram

Lango: Language Lego

intro: Lango is a natural language processing library for working with the building blocks of language.
github: https://github.com/ayoungprogrammer/Lango

Sequence-to-Sequence Learning with Attentional Neural Networks

github(Torch): https://github.com/harvardnlp/seq2seq-attn

harvardnlp code

intro: pen-source implementations of popular deep learning techniques with applications to NLP
homepage: http://nlp.seas.harvard.edu/code/

Seq2seq: Sequence to Sequence Learning with Keras

github: https://github.com/farizrahman4u/seq2seq

debug seq2seq

github: https://github.com/nicolas-ivanov/debug_seq2seq

Recurrent & convolutional neural network modules

intro: This repo contains Theano implementations of popular neural network components and optimization methods.
github: https://github.com/taolei87/rcnn

Datasets

Datasets for Natural Language Processing

github: https://github.com/karthikncode/nlp-datasets

Blogs

How to read: Character level deep learning

blog: https://offbit.github.io/how-to-read/
github: https://github.com/offbit/char-models

Heavy Metal and Natural Language Processing

part 1: http://www.degeneratestate.org/posts/2016/Apr/20/heavy-metal-and-natural-language-processing-part-1/

Sequence To Sequence Attention Models In PyCNN

https://talbaumel.github.io/Neural+Attention+Mechanism.html

Source Code Classification Using Deep Learning

http://blog.aylien.com/source-code-classification-using-deep-learning/

My Process for Learning Natural Language Processing with Deep Learning

https://medium.com/@MichaelTeifel/my-process-for-learning-natural-language-processing-with-deep-learning-bd0a64a36086

Convolutional Methods for Text

https://medium.com/@TalPerry/convolutional-methods-for-text-d5260fd5675f

Word2Vec

Word2Vec Tutorial - The Skip-Gram Model

http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/

Word2Vec Tutorial Part 2 - Negative Sampling

http://mccormickml.com/2017/01/11/word2vec-tutorial-part-2-negative-sampling/

Word2Vec Resources

http://mccormickml.com/2016/04/27/word2vec-resources/

Demos

AskImage.org - Deep Learning for Answering Questions about Images

homepage: http://www.askimage.org/

Talks / Videos

Navigating Natural Language Using Reinforcement Learning

youtube: https://www.youtube.com/watch?v=7s-erJbCkaY

Resources

So, you need to understand language data? Open-source NLP software can help!

blog: http://entopix.com/so-you-need-to-understand-language-data-open-source-nlp-software-can-help.html

Curated list of resources on building bots

github: https://github.com/hackerkid/bots

Notes for deep learning on NLP

https://medium.com/@frank_chung/notes-for-deep-learning-on-nlp-94ddfcb45723#.iouo0v7m7