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Title: GitHub - Amberiota/NLP-progress: Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks. · GitHub

Open Graph Title: GitHub - Amberiota/NLP-progress: Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.

X Title: GitHub - Amberiota/NLP-progress: Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.

Description: Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks. - Amberiota/NLP-progress

Open Graph Description: Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks. - Amberiota/NLP-progress

X Description: Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks. - Amberiota/NLP-progress

Opengraph URL: https://github.com/Amberiota/NLP-progress

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Domain: github.com

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LICENSE	https://github.com/Amberiota/NLP-progress/blob/master/LICENSE
LICENSE	https://github.com/Amberiota/NLP-progress/blob/master/LICENSE
README.md	https://github.com/Amberiota/NLP-progress/blob/master/README.md
README.md	https://github.com/Amberiota/NLP-progress/blob/master/README.md
README	https://github.com/Amberiota/NLP-progress
License	https://github.com/Amberiota/NLP-progress
	https://github.com/Amberiota/NLP-progress#tracking-progress-in-natural-language-processing
	https://github.com/Amberiota/NLP-progress#contributing
	https://github.com/Amberiota/NLP-progress#things-to-do
readthedocs	https://github.com/rtfd/readthedocs.org
UD	https://github.com/Amberiota/NLP-progress#ud
	https://github.com/Amberiota/NLP-progress#table-of-contents
CCG supertagging	https://github.com/Amberiota/NLP-progress#ccg-supertagging
CCGBank	https://github.com/Amberiota/NLP-progress#ccgbank
Chunking	https://github.com/Amberiota/NLP-progress#chunking
Penn Treebank	https://github.com/Amberiota/NLP-progress#penn-treebank%E2%80%94chunking
Constituency parsing	https://github.com/Amberiota/NLP-progress#constituency-parsing
Penn Treebank	https://github.com/Amberiota/NLP-progress#penn-treebank%E2%80%94constituency-parsing
Coreference resolution	https://github.com/Amberiota/NLP-progress#coreference-resolution
CoNLL 2012	https://github.com/Amberiota/NLP-progress#conll-2012
Dependency parsing	https://github.com/Amberiota/NLP-progress#dependency-parsing
Penn Treebank	https://github.com/Amberiota/NLP-progress#penn-treebank%E2%80%94dependency-parsing
Dialog	https://github.com/Amberiota/NLP-progress#dialog
Second dialog state tracking challenge	https://github.com/Amberiota/NLP-progress#second-dialog-state-tracking-challenge
Domain adaptation	https://github.com/Amberiota/NLP-progress#domain-adaptation
Multi-Domain Sentiment Dataset	https://github.com/Amberiota/NLP-progress#multi-domain-sentiment-dataset
Language modelling	https://github.com/Amberiota/NLP-progress#language-modeling
Penn Treebank	https://github.com/Amberiota/NLP-progress#penn-treebank%E2%80%94language-modeling
WikiText-2	https://github.com/Amberiota/NLP-progress#wikitext-2
Machine translation	https://github.com/Amberiota/NLP-progress#machine-translation
WMT 2014 EN-DE	https://github.com/Amberiota/NLP-progress#wmt-2014-en-de
WMT 2014 EN-FR	https://github.com/Amberiota/NLP-progress#wmt-2014-en-fr
Multi-task learning	https://github.com/Amberiota/NLP-progress#multi-task-learning
GLUE	https://github.com/Amberiota/NLP-progress#glue
Named entity recognition	https://github.com/Amberiota/NLP-progress#named-entity-recognition
CoNLL 2003	https://github.com/Amberiota/NLP-progress#conll-2003
Natural language inference	https://github.com/Amberiota/NLP-progress#natural-language-inference
SNLI	https://github.com/Amberiota/NLP-progress#snli
MultiNLI	https://github.com/Amberiota/NLP-progress#multinli
SciTail	https://github.com/Amberiota/NLP-progress#scitail
Part-of-speech tagging	https://github.com/Amberiota/NLP-progress#part-of-speech-tagging
UD	https://github.com/Amberiota/NLP-progress#ud
WSJ	https://github.com/Amberiota/NLP-progress#penn-treebank%E2%80%94pos-tagging
Reading comprehension	https://github.com/Amberiota/NLP-progress#reading-comprehension-/-question-answering
ARC	https://github.com/Amberiota/NLP-progress#arc
CNN / Daily Mail	https://github.com/Amberiota/NLP-progress#cnn-/-daily-mail%E2%80%94reading-comprehension
QAngaroo	https://github.com/Amberiota/NLP-progress#qangaroo
RACE	https://github.com/Amberiota/NLP-progress#race
SQuAD	https://github.com/Amberiota/NLP-progress#squad
Story Cloze Test	https://github.com/Amberiota/NLP-progress#story-cloze-test
Winograd Schema Challenge	https://github.com/Amberiota/NLP-progress#winograd-schema-challenge
Semantic textual similarity	https://github.com/Amberiota/NLP-progress#semantic-textual-similarity
SentEval	https://github.com/Amberiota/NLP-progress#senteval
Quora Question Pairs	https://github.com/Amberiota/NLP-progress#quora-question-pairs
Sentiment analysis	https://github.com/Amberiota/NLP-progress#sentiment-analysis
IMDb	https://github.com/Amberiota/NLP-progress#imdb
Sentihood	https://github.com/Amberiota/NLP-progress#sentihood
SST	https://github.com/Amberiota/NLP-progress#sst
Yelp	https://github.com/Amberiota/NLP-progress#yelp
Semantic parsing	https://github.com/Amberiota/NLP-progress#semantic-parsing
WikiSQL	https://github.com/Amberiota/NLP-progress#wikisql
Semantic role labeling	https://github.com/Amberiota/NLP-progress#semantic-role-labeling
OntoNotes	https://github.com/Amberiota/NLP-progress#ontonotes%E2%80%94semantic-role-labeling
Summarization	https://github.com/Amberiota/NLP-progress#summarization
CNN / Daily Mail	https://github.com/Amberiota/NLP-progress#cnn-/-daily-mail%E2%80%94summarization
Text classification	https://github.com/Amberiota/NLP-progress#text-classification
AG News	https://github.com/Amberiota/NLP-progress#ag-news
DBpedia	https://github.com/Amberiota/NLP-progress#dbpedia
TREC	https://github.com/Amberiota/NLP-progress#trec
	https://github.com/Amberiota/NLP-progress#ccg-supertagging
Steedman, 2000	http://www.citeulike.org/group/14833/article/8971002
Clark and Curran (2007)	https://www.mitpressjournals.org/doi/abs/10.1162/coli.2007.33.4.493
	https://github.com/Amberiota/NLP-progress#ccgbank
Hockenmaier and Steedman (2007)	http://www.aclweb.org/anthology/J07-3004
LSTM CCG Parsing	https://aclweb.org/anthology/N/N16/N16-1026.pdf
Supertagging with LSTMs	https://aclweb.org/anthology/N/N16/N16-1027.pdf
Deep multi-task learning with low level tasks supervised at lower layers	http://anthology.aclweb.org/P16-2038
CCG Supertagging with a Recurrent Neural Network	http://www.aclweb.org/anthology/P15-2041
	https://github.com/Amberiota/NLP-progress#chunking
	https://github.com/Amberiota/NLP-progress#penn-treebankchunking
Penn Treebank	https://catalog.ldc.upenn.edu/LDC99T42
Deep multi-task learning with low level tasks supervised at lower layers	http://anthology.aclweb.org/P16-2038
Semi-Supervised Sequential Labeling and Segmentation using Giga-word Scale Unlabeled Data	https://aclanthology.info/pdf/P/P08/P08-1076.pdf
	https://github.com/Amberiota/NLP-progress#constituency-parsing
phrase structure grammar	https://en.wikipedia.org/wiki/Phrase_structure_grammar
Recent approaches	https://papers.nips.cc/paper/5635-grammar-as-a-foreign-language.pdf
	https://github.com/Amberiota/NLP-progress#penn-treebankconstituency-parsing
Penn Treebank	https://catalog.ldc.upenn.edu/LDC99T42
Kitaev and Klein (2018)	https://arxiv.org/abs/1805.01052
Constituency Parsing with a Self-Attentive Encoder	https://arxiv.org/abs/1805.01052
Improving Neural Parsing by Disentangling Model Combination and Reranking Effects	https://arxiv.org/abs/1707.03058
In-Order Transition-based Constituent Parsing	http://aclweb.org/anthology/Q17-1029
Parsing as Language Modeling	http://www.aclweb.org/anthology/D16-1257
What Do Recurrent Neural Network Grammars Learn About Syntax?	https://arxiv.org/abs/1611.05774
Recurrent Neural Network Grammars	https://www.aclweb.org/anthology/N16-1024
Attention Is All You Need	https://arxiv.org/abs/1706.03762
Grammar as a Foreign Language	https://papers.nips.cc/paper/5635-grammar-as-a-foreign-language.pdf
Effective Self-Training for Parsing	https://pdfs.semanticscholar.org/6f0f/64f0dab74295e5eb139c160ed79ff262558a.pdf
	https://github.com/Amberiota/NLP-progress#coreference-resolution
	https://github.com/Amberiota/NLP-progress#conll-2012
CoNLL-2012 shared task	http://www.aclweb.org/anthology/W12-4501
Higher-order Coreference Resolution with Coarse-to-fine Inference	http://aclweb.org/anthology/N18-2108
Deep contextualized word representatIions	https://arxiv.org/abs/1802.05365
End-to-end Neural Coreference Resolution	https://arxiv.org/abs/1707.07045
	https://github.com/Amberiota/NLP-progress#dependency-parsing
	https://github.com/Amberiota/NLP-progress#penn-treebankdependency-parsing
Stanford Dependency	https://nlp.stanford.edu/software/dependencies_manual.pdf
What Do Recurrent Neural Network Grammars Learn About Syntax?	https://arxiv.org/abs/1611.05774
Parsing as Language Modeling	http://www.aclweb.org/anthology/D16-1257
Deep Biaffine Attention for Neural Dependency Parsing	https://arxiv.org/abs/1611.01734
Globally Normalized Transition-Based Neural Networks	https://www.aclweb.org/anthology/P16-1231
Distilling an Ensemble of Greedy Dependency Parsers into One MST Parser	https://arxiv.org/abs/1609.07561
Structured Training for Neural Network Transition-Based Parsing	http://anthology.aclweb.org/P/P15/P15-1032.pdf
Training with Exploration Improves a Greedy Stack-LSTM Parser	https://arxiv.org/abs/1603.03793
Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations	https://aclweb.org/anthology/Q16-1023
	https://github.com/Amberiota/NLP-progress#dialog
	https://github.com/Amberiota/NLP-progress#second-dialog-state-tracking-challenge
second dialog state tracking challenge	http://www.aclweb.org/anthology/W14-4337
Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems	https://arxiv.org/abs/1804.06512
Neural Belief Tracker: Data-Driven Dialogue State Tracking	https://arxiv.org/abs/1606.03777
Robust dialog state tracking using delexicalised recurrent neural networks and unsupervised gate	http://svr-ftp.eng.cam.ac.uk/~sjy/papers/htyo14.pdf
	https://github.com/Amberiota/NLP-progress#domain-adaptation
	https://github.com/Amberiota/NLP-progress#multi-domain-sentiment-dataset
Multi-Domain Sentiment Dataset	https://www.cs.jhu.edu/~mdredze/datasets/sentiment/
Strong Baselines for Neural Semi-supervised Learning under Domain Shift	https://arxiv.org/abs/1804.09530
Asymmetric Tri-training for Unsupervised Domain Adaptation	https://arxiv.org/abs/1702.08400
The Variational Fair Autoencoder	https://arxiv.org/abs/1511.00830
Domain-Adversarial Training of Neural Networks	https://arxiv.org/abs/1505.07818
	https://github.com/Amberiota/NLP-progress#language-modeling
	https://github.com/Amberiota/NLP-progress#penn-treebanklanguage-modeling
Mikolov et al. (2010)	http://www.fit.vutbr.cz/research/groups/speech/publi/2010/mikolov_interspeech2010_IS100722.pdf
Breaking the Softmax Bottleneck: A High-Rank RNN Language Model	https://arxiv.org/abs/1711.03953
Dynamic Evaluation of Neural Sequence Models	https://arxiv.org/abs/1709.07432
Regularizing and Optimizing LSTM Language Models	https://arxiv.org/abs/1708.02182
Breaking the Softmax Bottleneck: A High-Rank RNN Language Model	https://arxiv.org/abs/1711.03953
Regularizing and Optimizing LSTM Language Models	https://arxiv.org/abs/1708.02182
	https://github.com/Amberiota/NLP-progress#wikitext-2
WikiText-2	https://arxiv.org/abs/1609.07843
Breaking the Softmax Bottleneck: A High-Rank RNN Language Model	https://arxiv.org/abs/1711.03953
Dynamic Evaluation of Neural Sequence Models	https://arxiv.org/abs/1709.07432
Regularizing and Optimizing LSTM Language Models	https://arxiv.org/abs/1708.02182
Breaking the Softmax Bottleneck: A High-Rank RNN Language Model	https://arxiv.org/abs/1711.03953
Regularizing and Optimizing LSTM Language Models	https://arxiv.org/abs/1708.02182
	https://github.com/Amberiota/NLP-progress#machine-translation
Chen et al. (2018)	https://arxiv.org/abs/1804.09849
	https://github.com/Amberiota/NLP-progress#wmt-2014-en-de
The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation	https://arxiv.org/abs/1804.09849
Attention Is All You Need	https://arxiv.org/abs/1706.03762
Attention Is All You Need	https://arxiv.org/abs/1706.03762
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer	https://arxiv.org/abs/1701.06538
Convolutional Sequence to Sequence Learning	https://arxiv.org/abs/1705.03122
	https://github.com/Amberiota/NLP-progress#wmt-2014-en-fr
The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation	https://arxiv.org/abs/1804.09849
Attention Is All You Need	https://arxiv.org/abs/1706.03762
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer	https://arxiv.org/abs/1701.06538
Convolutional Sequence to Sequence Learning	https://arxiv.org/abs/1705.03122
Attention Is All You Need	https://arxiv.org/abs/1706.03762
	https://github.com/Amberiota/NLP-progress#multi-task-learning
	https://github.com/Amberiota/NLP-progress#glue
General Language Understanding Evaluation benchmark	https://arxiv.org/abs/1804.07461
GLUE leaderboard	https://gluebenchmark.com/leaderboard
	https://github.com/Amberiota/NLP-progress#named-entity-recognition
	https://github.com/Amberiota/NLP-progress#conll-2003
CoNLL 2003 NER task	http://www.aclweb.org/anthology/W03-0419.pdf
Deep contextualized word representations	https://arxiv.org/abs/1802.05365
Semi-supervised sequence tagging with bidirectional language models	https://arxiv.org/abs/1705.00108
Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks	https://arxiv.org/abs/1703.06345
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF	https://arxiv.org/abs/1603.01354
Neural Architectures for Named Entity Recognition	https://arxiv.org/abs/1603.01360
	https://github.com/Amberiota/NLP-progress#natural-language-inference
	https://github.com/Amberiota/NLP-progress#snli
Stanford Natural Language Inference (SNLI) Corpus	https://arxiv.org/abs/1508.05326
SNLI website	https://nlp.stanford.edu/projects/snli/
	https://github.com/Amberiota/NLP-progress#multinli
Multi-Genre Natural Language Inference (MultiNLI) corpus	https://arxiv.org/abs/1704.05426
MultiNLI	https://www.nyu.edu/projects/bowman/multinli/
in-genre (matched)	https://www.kaggle.com/c/multinli-matched-open-evaluation/leaderboard
cross-genre (mismatched)	https://www.kaggle.com/c/multinli-mismatched-open-evaluation/leaderboard
Improving Language Understanding by Generative Pre-Training	https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding	https://arxiv.org/abs/1804.07461
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning	https://arxiv.org/abs/1804.00079
	https://github.com/Amberiota/NLP-progress#scitail
SciTail	http://ai2-website.s3.amazonaws.com/publications/scitail-aaai-2018_cameraready.pdf
Improving Language Understanding by Generative Pre-Training	https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf
A Compare-Propagate Architecture with Alignment Factorization for Natural Language Inference	https://arxiv.org/abs/1801.00102
	https://github.com/Amberiota/NLP-progress#part-of-speech-tagging
	https://github.com/Amberiota/NLP-progress#ud
Universal Dependencies (UD)	http://universaldependencies.org/
Robust Multilingual Part-of-Speech Tagging via Adversarial Training	https://arxiv.org/abs/1711.04903
Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Loss	https://arxiv.org/abs/1604.05529
A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing	https://arxiv.org/abs/1705.05952
	https://github.com/Amberiota/NLP-progress#penn-treebankpos-tagging
Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation	https://www.aclweb.org/anthology/D/D15/D15-1176.pdf
Robust Multilingual Part-of-Speech Tagging via Adversarial Training	https://arxiv.org/abs/1711.04903
Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks	https://arxiv.org/abs/1703.06345
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF	https://arxiv.org/abs/1603.01354
Supertagging with LSTMs	https://aclweb.org/anthology/N/N16/N16-1027.pdf
Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation	https://www.aclweb.org/anthology/D/D15/D15-1176.pdf
Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Loss	https://arxiv.org/abs/1604.05529
	https://github.com/Amberiota/NLP-progress#reading-comprehension--question-answering
overview of reading comprehension tasks	https://uclmr.github.io/ai4exams/data.html
	https://github.com/Amberiota/NLP-progress#arc
AI2 Reasoning Challenge (ARC)	http://ai2-website.s3.amazonaws.com/publications/AI2ReasoningChallenge2018.pdf
ARC website	http://data.allenai.org/arc/
	https://github.com/Amberiota/NLP-progress#cnn--daily-mailreading-comprehension
CNN / Daily Mail dataset	https://arxiv.org/abs/1506.03340
Close-style	https://en.wikipedia.org/wiki/Cloze_test
A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task	https://www.aclweb.org/anthology/P16-1223
A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task	https://www.aclweb.org/anthology/P16-1223
Teaching Machines to Read and Comprehend	https://arxiv.org/abs/1506.03340
	https://github.com/Amberiota/NLP-progress#qangaroo
QAngaroo	http://qangaroo.cs.ucl.ac.uk/index.html
QAngaroo website	http://qangaroo.cs.ucl.ac.uk/leaderboard.html
	https://github.com/Amberiota/NLP-progress#race
RACE dataset	https://arxiv.org/abs/1704.04683
here	http://www.cs.cmu.edu/~glai1/data/race/
Improving Language Understanding by Generative Pre-Training	https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf
Multi-range Reasoning for Machine Comprehension	https://arxiv.org/abs/1803.09074
	https://github.com/Amberiota/NLP-progress#squad
Stanford Question Answering Dataset (SQuAD)	https://arxiv.org/abs/1606.05250
SQuAD 2.0	https://arxiv.org/abs/1806.03822
SQuAD website	https://rajpurkar.github.io/SQuAD-explorer/
	https://github.com/Amberiota/NLP-progress#story-cloze-test
Story Cloze Test	http://aclweb.org/anthology/W17-0906.pdf
Improving Language Understanding by Generative Pre-Training	https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf
Story Comprehension for Predicting What Happens Next	http://aclweb.org/anthology/D17-1168
A Simple and Effective Approach to the Story Cloze Test	http://aclweb.org/anthology/N18-2015
	https://github.com/Amberiota/NLP-progress#winograd-schema-challenge
Winograd Schema Challenge	https://www.aaai.org/ocs/index.php/KR/KR12/paper/view/4492
A Simple Method for Commonsense Reasoning	https://arxiv.org/abs/1806.02847
A Simple Method for Commonsense Reasoning	https://arxiv.org/abs/1806.02847
Combing Context and Commonsense Knowledge Through Neural Networks for Solving Winograd Schema Problems	https://aaai.org/ocs/index.php/SSS/SSS17/paper/view/15392
	https://github.com/Amberiota/NLP-progress#semantic-textual-similarity
	https://github.com/Amberiota/NLP-progress#senteval
SentEval	https://arxiv.org/abs/1803.05449
here	https://github.com/facebookresearch/SentEval
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning	https://arxiv.org/abs/1804.00079
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data	https://arxiv.org/abs/1705.02364
	https://github.com/Amberiota/NLP-progress#quora-question-pairs
Quora Question Pairs dataset	https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs
Neural Paraphrase Identification of Questions with Noisy Pretraining	https://arxiv.org/abs/1704.04565
Bilateral Multi-Perspective Matching for Natural Language Sentences	https://arxiv.org/abs/1702.03814
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning	https://arxiv.org/abs/1804.00079
	https://github.com/Amberiota/NLP-progress#sentiment-analysis
	https://github.com/Amberiota/NLP-progress#imdb
IMDb dataset	https://ai.stanford.edu/~ang/papers/acl11-WordVectorsSentimentAnalysis.pdf
Universal Language Model Fine-tuning for Text Classification	https://arxiv.org/abs/1801.06146
Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings	https://arxiv.org/abs/1602.02373
Adversarial Training Methods for Semi-Supervised Text Classification	https://arxiv.org/abs/1605.07725
Learned in Translation: Contextualized Word Vectors	https://arxiv.org/abs/1708.00107
	https://github.com/Amberiota/NLP-progress#sentihood
Sentihood	http://www.aclweb.org/anthology/C16-1146
Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-based Sentiment Analysis	http://aclweb.org/anthology/N18-2045
Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM	http://sentic.net/sentic-lstm.pdf
Sentihood: Targeted aspect based sentiment analysis dataset for urban neighbourhoods	http://www.aclweb.org/anthology/C16-1146
	https://github.com/Amberiota/NLP-progress#sst
Stanford Sentiment Treebank	https://nlp.stanford.edu/sentiment/index.html
Deep contextualized word representations	https://arxiv.org/abs/1802.05365
Learned in Translation: Contextualized Word Vectors	https://arxiv.org/abs/1708.00107
Learning to Generate Reviews and Discovering Sentiment	https://arxiv.org/abs/1704.01444
Learned in Translation: Contextualized Word Vectors	https://arxiv.org/abs/1708.00107
Neural Semantic Encoders	http://www.aclweb.org/anthology/E17-1038
Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling	http://www.aclweb.org/anthology/C16-1329
	https://github.com/Amberiota/NLP-progress#yelp
Yelp Review dataset	https://papers.nips.cc/paper/5782-character-level-convolutional-networks-for-text-classification.pdf
Universal Language Model Fine-tuning for Text Classification	https://arxiv.org/abs/1801.06146
Deep Pyramid Convolutional Neural Networks for Text Categorization	http://aclweb.org/anthology/P17-1052
Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings	https://arxiv.org/abs/1602.02373
Character-level Convolutional Networks for Text Classification	https://papers.nips.cc/paper/5782-character-level-convolutional-networks-for-text-classification.pdf
Universal Language Model Fine-tuning for Text Classification	https://arxiv.org/abs/1801.06146
Deep Pyramid Convolutional Neural Networks for Text Categorization	http://aclweb.org/anthology/P17-1052
Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings	https://arxiv.org/abs/1602.02373
Character-level Convolutional Networks for Text Classification	https://papers.nips.cc/paper/5782-character-level-convolutional-networks-for-text-classification.pdf
	https://github.com/Amberiota/NLP-progress#semantic-parsing
Abstract Meaning Representation (AMR)	https://en.wikipedia.org/wiki/Abstract_Meaning_Representation
	https://github.com/Amberiota/NLP-progress#wikisql
WikiSQL dataset	https://arxiv.org/abs/1709.00103
TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation	https://arxiv.org/abs/1804.09769
Sqlnet: Generating structured queries from natural language without reinforcement learning	https://arxiv.org/abs/1711.04436
Seq2sql: Generating structured queries from natural language using reinforcement learning	https://arxiv.org/abs/1709.00103
	https://github.com/Amberiota/NLP-progress#semantic-role-labeling
	https://github.com/Amberiota/NLP-progress#ontonotessemantic-role-labeling
OntoNotes benchmark	http://www.aclweb.org/anthology/W13-3516
Deep contextualized word representations	https://arxiv.org/abs/1802.05365
Deep Semantic Role Labeling: What Works and What’s Next	http://aclweb.org/anthology/P17-1044
	https://github.com/Amberiota/NLP-progress#summarization
	https://github.com/Amberiota/NLP-progress#cnn--daily-mailsummarization
CNN / Daily Mail dataset	https://arxiv.org/abs/1506.03340
Nallapati et al. (2016)	http://www.aclweb.org/anthology/K16-1028
Deep Communicating Agents for Abstractive Summarization	https://arxiv.org/abs/1803.10357
Get To The Point: Summarization with Pointer-Generator Networks	https://arxiv.org/abs/1704.04368
SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents	https://arxiv.org/abs/1611.04230
Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond	http://www.aclweb.org/anthology/K16-1028
	https://github.com/Amberiota/NLP-progress#text-classification
	https://github.com/Amberiota/NLP-progress#ag-news
AG News corpus	https://papers.nips.cc/paper/5782-character-level-convolutional-networks-for-text-classification.pdf
AG's corpus of news articles on the web	http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html
Universal Language Model Fine-tuning for Text Classification	https://arxiv.org/abs/1801.06146
Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings	https://arxiv.org/abs/1602.02373
Deep Pyramid Convolutional Neural Networks for Text Categorization	http://aclweb.org/anthology/P17-1052
Very Deep Convolutional Networks for Text Classification	https://arxiv.org/abs/1606.01781
Character-level Convolutional Networks for Text Classification	https://papers.nips.cc/paper/5782-character-level-convolutional-networks-for-text-classification.pdf
	https://github.com/Amberiota/NLP-progress#dbpedia
DBpedia ontology	https://papers.nips.cc/paper/5782-character-level-convolutional-networks-for-text-classification.pdf
Universal Language Model Fine-tuning for Text Classification	https://arxiv.org/abs/1801.06146
Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings	https://arxiv.org/abs/1602.02373
Deep Pyramid Convolutional Neural Networks for Text Categorization	http://aclweb.org/anthology/P17-1052
Very Deep Convolutional Networks for Text Classification	https://arxiv.org/abs/1606.01781
Character-level Convolutional Networks for Text Classification	https://papers.nips.cc/paper/5782-character-level-convolutional-networks-for-text-classification.pdf
	https://github.com/Amberiota/NLP-progress#trec
TREC dataset	http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.11.2766&rep=rep1&type=pdf
Universal Language Model Fine-tuning for Text Classification	https://arxiv.org/abs/1801.06146
Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling	http://www.aclweb.org/anthology/C16-1329
Discriminative Neural Sentence Modeling by Tree-Based Convolution	http://aclweb.org/anthology/D15-1279
Learned in Translation: Contextualized Word Vectors	https://arxiv.org/abs/1708.00107
High Accuracy Rule-based Question Classification using Question Syntax and Semantics	http://www.aclweb.org/anthology/C16-1116
Improving Question Classification by Feature Extraction and Selection	https://www.researchgate.net/publication/303553351_Improving_Question_Classification_by_Feature_Extraction_and_Selection
Readme	https://github.com/Amberiota/NLP-progress#readme-ov-file
MIT license	https://github.com/Amberiota/NLP-progress#MIT-1-ov-file
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