BERT/Transformer/迁移学习NLP资源大列表

2019 年 6 月 9 日 专知

【导读】cedrickchee维护这个项目包含用于自然语言处理(NLP)的大型机器(深度)学习资源，重点关注转换器(BERT)的双向编码器表示、注意机制、转换器架构/网络和NLP中的传输学习。

https://github.com/cedrickchee/awesome-bert-nlp

Papers

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context by Zihang Dai, Zhilin Yang, Yiming Yang, William W. Cohen, Jaime Carbonell, Quoc V. Le and Ruslan Salakhutdinov.

Uses smart caching to improve the learning of long-term dependency in Transformer. Key results: state-of-art on 5 language modeling benchmarks, including ppl of 21.8 on One Billion Word (LM1B) and 0.99 on enwiki8. The authors claim that the method is more flexible, faster during evaluation (1874 times speedup), generalizes well on small datasets, and is effective at modeling short and long sequences.

Conditional BERT Contextual Augmentation by Xing Wu, Shangwen Lv, Liangjun Zang, Jizhong Han and Songlin Hu.
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering by Chenguang Zhu, Michael Zeng and Xuedong Huang.
Language Models are Unsupervised Multitask Learners by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever.
The Evolved Transformer by David R. So, Chen Liang and Quoc V. Le.

They used architecture search to improve Transformer architecture. Key is to use evolution and seed initial population with Transformer itself. The architecture is better and more efficient, especially for small size models.

Open Sourcing BERT: State-of-the-Art Pre-training for Natural Language Processing from Google AI.
The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning).
Dissecting BERT by Miguel Romero and Francisco Ingham - Understand BERT in depth with an intuitive, straightforward explanation of the relevant concepts.
A Light Introduction to Transformer-XL.
Generalized Language Models by Lilian Weng, Research Scientist at OpenAI.

The Annotated Transformer by Harvard NLP Group - Further reading to understand the "Attention is all you need" paper.
Attention? Attention! - Attention guide by Lilian Weng from OpenAI.
Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention) by Jay Alammar, an Instructor from Udacity ML Engineer Nanodegree.

The Transformer blog post.
The Illustrated Transformer by Jay Alammar, an Instructor from Udacity ML Engineer Nanodegree.
Watch Łukasz Kaiser’s talk walking through the model and its details.
Transformer-XL: Unleashing the Potential of Attention Models by Google Brain.
Generative Modeling with Sparse Transformers by OpenAI - an algorithmic improvement of the attention mechanism to extract patterns from sequences 30x longer than possible previously.

Better Language Models and Their Implications.
Improving Language Understanding with Unsupervised Learning - this is an overview of the original GPT model.
🦄 How to build a State-of-the-Art Conversational AI with Transfer Learning by Hugging Face.

huggingface/pytorch-pretrained-BERT - A PyTorch implementation of Google AI's BERT model with script to load Google's pre-trained models by Hugging Face.
codertimo/BERT-pytorch - Google AI 2018 BERT pytorch implementation.
innodatalabs/tbert - PyTorch port of BERT ML model.
kimiyoung/transformer-xl - Code repository associated with the Transformer-XL paper.
dreamgonfly/BERT-pytorch - PyTorch implementation of BERT in "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding".
dhlee347/pytorchic-bert - Pytorch implementation of Google BERT

Separius/BERT-keras - Keras implementation of BERT with pre-trained weights.
CyberZHG/keras-bert - Implementation of BERT that could load official pre-trained models for feature extraction and prediction.

guotong1988/BERT-tensorflow - BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.
kimiyoung/transformer-xl - Code repository associated with the Transformer-XL paper.