Natural Language Processing
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
Skip-Thought Vectors
A Primer on Neural Network Models for Natural Language Processing
Character-aware Neural Language Models
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
A Character-level Decoder without Explicit Segmentation for Neural Machine Translation
NEMATUS: Attention-based encoder-decoder model for neural machine translation
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
Linguistic Input Features Improve Neural Machine Translation
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
Tips on Building Neural Machine Translation Systems
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
Deep Character-Level Neural Machine Translation
Neural Machine Translation Implementations
Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
Learning to Translate in Real-time with Neural Machine Translation
Is Neural Machine Translation Ready for Deployment? A Case Study on 30 Translation Directions
Fully Character-Level Neural Machine Translation without Explicit Segmentation
Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation
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
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
MXNMT: MXNet based Neural Machine Translation
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
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
Attention-over-Attention Neural Networks for Reading Comprehension
Teaching Machines to Read and Comprehend CNN News and Children Books using Torch
Reasoning with Memory Augmented Neural Networks for Language Comprehension
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
Rationale-Augmented Convolutional Neural Networks for Text Classification
Text classification using DIGITS and Torch7
Recurrent Neural Network for Text Classification with Multi-Task Learning
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
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
Implementing a CNN for Text Classification in TensorFlow
fancy-cnn: Multiparadigm Sequential Convolutional Neural Networks for text classification
Convolutional Neural Networks for Text Categorization: Shallow Word-level vs. Deep Character-level
Tweet Classification using RNN and CNN
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
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
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.
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
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
Convolutional Residual Memory Networks
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
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
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
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
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
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)
Cruciform: Solving Crosswords with Natural Language Processing
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
NLP-Caffe: natural language processing with Caffe
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
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
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
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
Blogs
How to read: Character level deep learning
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
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
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
Notes for deep learning on NLP
https://medium.com/@frank_chung/notes-for-deep-learning-on-nlp-94ddfcb45723#.iouo0v7m7