New blog post! Use #rstats, Stanford's CoreNLP, cleanNLP, and tidytext to explore the most common and distinctive nouns and verbs in the Qur'an—all in Arabic! https://t.co/mqkca17a2r pic.twitter.com/hDFqWmeA5Y
— Andrew Heiss, PhD (@andrewheiss) December 28, 2018
Hyperbolic Deep Learning for Chinese Natural Language Understanding
— Thomas Lahore (@evolvingstuff) December 28, 2018
"first time in Chinese NLP that a character-based model outperformed its word-based counterpart"https://t.co/B5GV6U0UU3 pic.twitter.com/RCAGgOvFiP
Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond
— Yann LeCun (@ylecun) December 27, 2018
90 languages, all handled by a single encoder.
New SOTA on XNLI.
From FAIR-Paris. https://t.co/OcJkoFzdyM
textrecipes have finally hit CRAN 🎉 Integrate text preprocessing into your #tidymodels workflow 📚 Many thanks to @topepos for help and guidance! https://t.co/Fd43cOYK3l #rstats #tidyverse
— Emil Hvitfeldt (@Emil_Hvitfeldt) December 26, 2018
A Tutorial on Deep Latent Variable Models of Natural Language #EMNLP
— ML Review (@ml_review) December 21, 2018
By @HarvardNLP
Notebookhttps://t.co/JRpINozCA4
ArXiv [48pp]https://t.co/yUMy65GbZn pic.twitter.com/SWf9NVOHGV
The author of this paper-classifier ran their model on their paper describing this paper-classifier and it suggested a strong reject. 🤣
— hardmaru (@hardmaru) December 21, 2018
We can probably train a better model using data from @karpathy’s arxiv-sanity.https://t.co/LgbMxWAXZj pic.twitter.com/4WJG7tP4jt
10 Exciting Ideas of 2018 in NLP: A collection of 10 ideas that I found exciting and impactful this year—and that we'll likely see more of in the future.https://t.co/iv29bxYbq4 pic.twitter.com/wqCE2Duf6M
— Sebastian Ruder (@seb_ruder) December 19, 2018
TextBugger: Generating Adversarial Text Against Real-world Applications #NDSS2019
— ML Review (@ml_review) December 19, 2018
1. Outperforms SoTA in attack success rate
2. Preserves the utility of benign text, ~95% of text human comprehendible
3. Comp. complexity sub-linear to the text lengthhttps://t.co/ihiR3g32wk pic.twitter.com/xPnzXBKRp7
NEW POST: Word associations from the Small World of Words 📜 #rstatshttps://t.co/YGqCiINQYq pic.twitter.com/JI7tAMaNDo
— Julia Silge (@juliasilge) December 16, 2018
1/5 Interested in learning how to collect and analyze social media data using topic models, text networks, or word2vec? I'm pleased to announce I am releasing an open source version of my "Text as Data" class from Duke's Data Science program: https://t.co/k2ScUDWVoJ pic.twitter.com/qgM8xEjsIn
— Chris Bail (@chris_bail) December 14, 2018
PyText from @facebookai : A set of NLP models that are highly configurable, that have been shipping at scale, along with efficient inference engine that runs them. Aimed at product developers rather than researchers.https://t.co/9xatvwTd8V pic.twitter.com/n5NiZ6z5QS
— PyTorch (@PyTorch) December 14, 2018
Check out @danqi_chen's just published PhD thesis for an up-to-date overview of the world (and future) of neural reading comprehension 👏🏻https://t.co/dKLaWc7gBt pic.twitter.com/FwMiP1ls8G
— Sebastian Ruder (@seb_ruder) December 14, 2018
