NLP Guide / 2017
This is an open guide to the noteworthy happenings in natural language processing in 2017.
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advances in generalisable DL
char-level - not just for lang id and translit
unsupervised approaches, e.g. cross-lingual word embeddings
NMT beats SMT
EFF AI Metrics - Written Language, Spoken Language
https://www.eff.org/ai/metrics
AI Index - Natural Language Understanding
https://aiindex.org/2017-report.pdf
“Poincaré Embeddings for Learning Hierarchical Representations” - Facebook
https://arxiv.org/pdf/1705.08039.pdf
“Attention Is All You Need” Google
https://arxiv.org/abs/1706.03762
“One Model To Learn Them All” Google
https://arxiv.org/abs/1706.05137
SQuAD leaderboard
https://rajpurkar.github.io/SQuAD-explorer/
“Interpreting neurons in an LSTM network” YerevaNN http://yerevann.github.io/2017/06/27/interpreting-neurons-in-an-LSTM-network/
Quora Question Pairs
https://www.kaggle.com/c/quora-question-pairs
data leakage problem
An Adversarial Review of “Adversarial Generation of Natural Language” - Yoav Goldberg https://medium.com/@yoav.goldberg/an-adversarial-review-of-adversarial-generation-of-natural-language-409ac3378bd7)
pytorch/text - PyTorch
MXNet sockeye - Amazon
MXNet Gluon - Amazon
InferSent - Facebook
ParlAI - Facebook
starSpace - Facebook
fastText lang id - Facebook
AllenNLP - Allen Institute
ABBYY Real-Time Recognition SDK
spaCy 2.0 - new features and new languages
Natural Language API - Google - new languages and new services
fastText pre-trained word vectors on Wikipedia for 294 languages
CLEVR
http://cs.stanford.edu/people/jcjohns/clevr/
https://arxiv.org/pdf/1612.06890.pdf
https://github.com/facebookresearch/clevr-iep
https://github.com/facebookresearch/clevr-dataset-gen
ACL
EMNLP
EACL
NIST TAC
PyData
RAAIS
NIPS
http://cs224n.stanford.edu / http://cs224d.stanford.edu
https://www.youtube.com/watch?v=OQQ-W_63UgQ&list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6
https://github.com/oxford-cs-deepnlp-2017/lectures
http://thestraightdope.mxnet.io/chapter05_recurrent-neural-networks/simple-rnn.html
http://pytorch.org/tutorials/beginner/deep_learning_nlp_tutorial.html
http://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html
https://blog.keras.io/a-ten-minute-introduction-to-sequence-to-sequence-learning-in-keras.html
Google Translate with NMT research.googleblog.com research.google.com wiki
Алиса - Yandex
DeepL Translator - NMT better than Google for 7 languages - https://www.deepl.com/translator
AWS Translate API preview - Amazon - https://aws.amazon.com/translate/
https://nlp.stanford.edu/read/
http://mitp.nautil.us/article/170/last-words-computational-linguistics-and-deep-learning
http://nathan.ai
https://yerevann.github.io/
http://approximatelycorrect.com/category/natural-language-processing/
http://approximatelycorrect.com/2017/09/26/a-random-walk-through-emnlp-2017/
http://newsletter.ruder.io/
http://ruder.io/highlights-emnlp-2017/index.html
http://ruder.io/word-embeddings-2017/
https://explosion.ai/blog/
https://explosion.ai/blog/quora-deep-text-pair-classification
https://www.producthunt.com/@bittlingmayer/collections
https://www.reddit.com/r/LanguageTechnology/
https://plus.google.com/communities/112547995826249627629