NLP经典论文-K6K4.com

Q：你读过哪些NLP相关的论文？挑一篇简单介绍一下

模型	全称	发布时间
Word2vec	Efficient Estimation of Word Representations in Vector Space	2013.01
Seq2seq	Sequence to Sequence Learning with Neural Networks	2014.09
Attention	Attention Is All You Need	2015.02
Transformer (vanilla)	Character-Level Language Modeling with Deeper Self-Attention	2017.06
ELMO	Deep contextualized word representations	2018.02
Transformer (universal)	Universal Transformers	2018.07
GPT	Improving Language Understanding by Generative Pre-Training	2018
BERT	BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding	2018.10
GPT-2	Language Models are Unsupervised Multitask Learners	2019.02
Transformer-XL	Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context	2019.06
XLNet	XLNet: Generalized Autoregressive Pretraining for Language Understanding	2019.06

以上很多模型都集成在huggingface/transformers库中：

https://github.com/huggingface/transformers

下载链接：

Word2vec

https://arxiv.org/pdf/1301.3781.pdf

https://github.com/danielfrg/word2vec

Seq2seq

https://arxiv.org/pdf/1409.3215.pdf

Attention

https://arxiv.org/abs/1706.03762.pdf

Transformer（vanilla）

https://arxiv.org/abs/1706.03762.pdf

https://github.com/tensorflow/tensor2tensor

ELMO

https://arxiv.org/abs/1802.05365.pdf

Transformer（universal）

https://arxiv.org/abs/1807.03819.pdf

GPT

https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf

Bert

https://arxiv.org/pdf/1810.04805.pdf

https://github.com/google-research/bert

GPT-2

https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf

https://github.com/openai/gpt-2

Transformer-XL

https://arxiv.org/abs/1901.02860.pdf

https://github.com/kimiyoung/transformer-xl

XLNet

https://arxiv.org/abs/1906.08237.pdf

https://github.com/zihangdai/xlnet

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