Tag: nlp

Congrats @jnhwkim and team on winning the VQA challenge at #CVPR2018.
Read their paper "Bilinear Attention Networks" at https://t.co/sqXNysYxcv
PyTorch based code at: https://t.co/XIOwVHfLtO https://t.co/FGOR3Pe9Rl

— PyTorch (@PyTorch) June 19, 2018

code and pre-trained models to reproduce the recent paper "Scaling Neural Machine Translation" (https://t.co/mrRDmlwax1) where we train on up to 128 GPUs with half precision floating point operations as well as delayed batching.

— PyTorch (@PyTorch) June 16, 2018

Facebook's fairseq MT engine is really, really fast... Like, 50% faster than @marian_nmt (which is itself way faster than Sockeye/OpenNMT/Tensor2Tensor/xnmt/Nematus/etc) at generating from the same Transformer model https://t.co/tWZuSog5gd

— James Bradbury (@jekbradbury) June 15, 2018

I made a @pytorch implementation of @openai's pretrained transformer with a script to import OpenAI's pre-trained model.

Link: https://t.co/6zY8NavPA3

Thanks @AlecRad, @karthik_r_n, @TimSalimans, @ilyasut for open-sourcing the code right away!

— Thomas Wolf (@Thom_Wolf) June 14, 2018

did terminology move and i didn't notice? do you consider language modeling (training a probabilistic model p(w1 w2 w3 ... wN)) to be unsupervised learning?

— Hal Daumé III (@haldaume3) June 12, 2018

Great work! Language models serving as the basis for transfer learning in task agnostic NLP is really going to feed the next generation of tools and transformations =]

— Smerity (@Smerity) June 11, 2018

This is exactly where we were hoping our ULMFit work would head - really great work from @OpenAI! 😊

If you're doing NLP and haven't tried language model transfer learning yet, then jump in now, because it's a Really Big Deal. https://t.co/0Dj8ChCxvu

— Jeremy Howard (@jeremyphoward) June 11, 2018

What I've been working on for the past year! https://t.co/CAQMYS1rR7

Inspired by CoVE, ELMo, and ULMFiT we show that a single transformer language model can be finetuned to a wide variety of NLP tasks and performs very well with little tuning/tweaking.

— Alec Radford (@AlecRad) June 11, 2018

We show large language models trained on massive text corpora (LM1b, CommonCrawl, Gutenberg) can be used for commonsense reasoning and obtain SOTA on Winograd Schema Challenge. Paper at https://t.co/aRndlByWfj, results reproducible at https://t.co/jFOmUYf03O pic.twitter.com/s3uyrksAQz

— Trieu H. Trinh (@thtrieu_) June 11, 2018

New NLP News - Conversational AI tutorial, RNNs for particle physics, InfoGAN, NLP Coursera, NLP book, killer robots, Code2pix, Google AI principles, relational reasoning https://t.co/5zT3QpjYAT

— Sebastian Ruder (@seb_ruder) June 11, 2018

Some of my thoughts on ai and the future of chat bots:https://t.co/a9Fm1AXFkY

— Richard (@RichardSocher) May 21, 2018

Slides for my keynote this morning on Successes and Frontiers of Deep Learning are now online. Mainly a high-level overview of some important and exciting application areas + some recent work on semi-supervised / transfer learning. https://t.co/uVcLtbHSuT

— Sebastian Ruder (@seb_ruder) May 21, 2018