NLP论文多个领域经典、顶会、必读整理分享及相关解读博客分享_icassp nlp论文

持续更新收集***，更多内容详见Github

1、Bert系列

• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding - NAACL 2019)
• ERNIE 2.0: A Continual Pre-training Framework for Language Understanding - arXiv 2019)
• StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding - arXiv 2019)
• RoBERTa: A Robustly Optimized BERT Pretraining Approach - arXiv 2019)
• ALBERT: A Lite BERT for Self-supervised Learning of Language Representations - arXiv 2019)
• Multi-Task Deep Neural Networks for Natural Language Understanding - arXiv 2019)
• What does BERT learn about the structure of language? (ACL2019)
• Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned (ACL2019) [github]
• Open Sesame: Getting Inside BERT's Linguistic Knowledge (ACL2019 WS)
• Analyzing the Structure of Attention in a Transformer Language Model (ACL2019 WS)
• What Does BERT Look At? An Analysis of BERT's Attention (ACL2019 WS)
• Do Attention Heads in BERT Track Syntactic Dependencies?
• Blackbox meets blackbox: Representational Similarity and Stability Analysis of Neural Language Models and Brains (ACL2019 WS)
• Inducing Syntactic Trees from BERT Representations (ACL2019 WS)
• A Multiscale Visualization of Attention in the Transformer Model (ACL2019 Demo)
• Visualizing and Measuring the Geometry of BERT
• How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings (EMNLP2019)
• Are Sixteen Heads Really Better than One? (NeurIPS2019)
• On the Validity of Self-Attention as Explanation in Transformer Models
• Visualizing and Understanding the Effectiveness of BERT (EMNLP2019)
• Attention Interpretability Across NLP Tasks
• Revealing the Dark Secrets of BERT (EMNLP2019)
• Investigating BERT's Knowledge of Language: Five Analysis Methods with NPIs (EMNLP2019)
• The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives (EMNLP2019)
• A Primer in BERTology: What we know about how BERT works
• Do NLP Models Know Numbers? Probing Numeracy in Embeddings (EMNLP2019)
• How Does BERT Answer Questions? A Layer-Wise Analysis of Transformer Representations (CIKM2019)
• Whatcha lookin' at? DeepLIFTing BERT's Attention in Question Answering
• What does BERT Learn from Multiple-Choice Reading Comprehension Datasets?
• Calibration of Pre-trained Transformers
• exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformers Models [github]
• MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices [github]
• 最前沿的12个NLP预训练模型
• NLP预训练模型：从transformer到albert
• XLNet:运行机制及和Bert的异同比较
• Bert时代的创新（应用篇）：Bert在NLP各领域的应用进展
•  

2、Transformer系列

• Attention Is All You Need - arXiv 2017)
• Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context - arXiv 2019)
• Universal Transformers - ICLR 2019)
• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer - arXiv 2019)
• Reformer: The Efficient Transformer - ICLR 2020)
• Adaptive Attention Span in Transformers (ACL2019)
• Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context (ACL2019) [github]
• Generating Long Sequences with Sparse Transformers
• Adaptively Sparse Transformers (EMNLP2019)
• Compressive Transformers for Long-Range Sequence Modelling
• The Evolved Transformer (ICML2019)
• Reformer: The Efficient Transformer (ICLR2020) [github]
• GRET: Global Representation Enhanced Transformer (AAAI2020)
• Transformer on a Diet [github]
• Efficient Content-Based Sparse Attention with Routing Transformers
• BP-Transformer: Modelling Long-Range Context via Binary Partitioning
• Recipes for building an open-domain chatbot
• Longformer: The Long-Document Transformer
• UnifiedQA: Crossing Format Boundaries With a Single QA System [github]
• 《Attention is All You Need》浅读（简介+代码）
• 通俗易懂Transformer
• 放弃幻想，全面拥抱Transformer：自然语言处理三大特征抽取器（CNN/RNN/TF）比较

3、迁移学习系列(Transfer Learning)

• Deep contextualized word representations - NAACL 2018)
• Universal Language Model Fine-tuning for Text Classification - ACL 2018)
• Improving Language Understanding by Generative Pre-Training - Alec Radford)
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding - NAACL 2019)
• Cloze-driven Pretraining of Self-attention Networks - arXiv 2019)
• Unified Language Model Pre-training for Natural Language Understanding and Generation - arXiv 2019)
• MASS: Masked Sequence to Sequence Pre-training for Language Generation - ICML 2019)
• MPNet: Masked and Permuted Pre-training for Language Understanding)[github]
• UNILMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training)[github]

4、文本摘要系列(Text Summarization)

• Positional Encoding to Control Output Sequence Length - Sho Takase(2019)
• Fine-tune BERT for Extractive Summarization - Yang Liu(2019)
• Language Models are Unsupervised Multitask Learners - Alec Radford(2019)
• A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss - Wan-Ting Hsu(2018)
• A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents - Arman Cohan(2018)
• GENERATING WIKIPEDIA BY SUMMARIZING LONG SEQUENCES - Peter J. Liu(2018)
• Get To The Point: Summarization with Pointer-Generator Networks - Abigail See(2017) * A Neural Attention Model for Sentence Summarization - Alexander M. Rush(2015)
• HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization (ACL2019)
• Deleter: Leveraging BERT to Perform Unsupervised Successive Text Compression
• Discourse-Aware Neural Extractive Model for Text Summarization
• PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization[github]
• Discourse-Aware Neural Extractive Text Summarization[github]

5、情感分析系列(Sentiment Analysis)

• Multi-Task Deep Neural Networks for Natural Language Understanding - Xiaodong Liu(2019)
• Aspect-level Sentiment Analysis using AS-Capsules - Yequan Wang(2019)
• On the Role of Text Preprocessing in Neural Network Architectures: An Evaluation Study on Text Categorization and Sentiment Analysis - Jose Camacho-Collados(2018)
• Learned in Translation: Contextualized Word Vectors - Bryan McCann(2018)
• Universal Language Model Fine-tuning for Text Classification - Jeremy Howard(2018)
• Convolutional Neural Networks with Recurrent Neural Filters - Yi Yang(2018)
• Information Aggregation via Dynamic Routing for Sequence Encoding - Jingjing Gong(2018)
• Learning to Generate Reviews and Discovering Sentiment - Alec Radford(2017)
• A Structured Self-attentive Sentence Embedding - Zhouhan Lin(2017)
• Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence (NAACL2019)
• BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis (NAACL2019)
• Exploiting BERT for End-to-End Aspect-based Sentiment Analysis (EMNLP2019 WS)
• Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification
• An Investigation of Transfer Learning-Based Sentiment Analysis in Japanese (ACL2019)
• "Mask and Infill" : Applying Masked Language Model to Sentiment Transfer
• Adversarial Training for Aspect-Based Sentiment Analysis with BERT
• Utilizing BERT Intermediate Layers for Aspect Based Sentiment Analysis and Natural Language Inference

6、问答&阅读理解&对话系统系列(Question Answering)

• Language Models are Unsupervised Multitask Learners - Alec Radford(2019)
• Improving Language Understanding by Generative Pre-Training - Alec Radford(2018)
• Bidirectional Attention Flow for Machine Comprehension - Minjoon Seo(2018)
• Reinforced Mnemonic Reader for Machine Reading Comprehension - Minghao Hu(2017)
• Neural Variational Inference for Text Processing - Yishu Miao(2015)
• A BERT Baseline for the Natural Questions
• MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension (ACL2019)
• Unsupervised Domain Adaptation on Reading Comprehension
• BERTQA -- Attention on Steroids
• A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning (EMNLP2019)
• SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
• Multi-hop Question Answering via Reasoning Chains
• Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple Documents
• Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering (EMNLP2019 WS)
• End-to-End Open-Domain Question Answering with BERTserini (NAALC2019)
• Latent Retrieval for Weakly Supervised Open Domain Question Answering (ACL2019)
• Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering (EMNLP2019)
• Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering (ICLR2020)
• Learning to Ask Unanswerable Questions for Machine Reading Comprehension (ACL2019)
• Unsupervised Question Answering by Cloze Translation (ACL2019)
• Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation
• A Recurrent BERT-based Model for Question Generation (EMNLP2019 WS)
• Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds
• Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension (ACL2019)
• Incorporating Relation Knowledge into Commonsense Reading Comprehension with Multi-task Learning (CIKM2019)
• SG-Net: Syntax-Guided Machine Reading Comprehension
• MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension
• Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning (EMNLP2019)
• ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning (ICLR2020)
• Robust Reading Comprehension with Linguistic Constraints via Posterior Regularization
• BAS: An Answer Selection Method Using BERT Language Model
• Beat the AI: Investigating Adversarial Human Annotations for Reading Comprehension
• A Simple but Effective Method to Incorporate Multi-turn Context with BERT for Conversational Machine Comprehension (ACL2019 WS)
• FlowDelta: Modeling Flow Information Gain in Reasoning for Conversational Machine Comprehension (ACL2019 WS)
• BERT with History Answer Embedding for Conversational Question Answering (SIGIR2019)
• GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension (ICML2019 WS)
• Beyond English-only Reading Comprehension: Experiments in Zero-Shot Multilingual Transfer for Bulgarian (RANLP2019)
• XQA: A Cross-lingual Open-domain Question Answering Dataset (ACL2019)
• Cross-Lingual Machine Reading Comprehension (EMNLP2019)
• Zero-shot Reading Comprehension by Cross-lingual Transfer Learning with Multi-lingual Language Representation Model
• Multilingual Question Answering from Formatted Text applied to Conversational Agents
• BiPaR: A Bilingual Parallel Dataset for Multilingual and Cross-lingual Reading Comprehension on Novels (EMNLP2019)
• MLQA: Evaluating Cross-lingual Extractive Question Answering
• Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension (TACL)
• SberQuAD - Russian Reading Comprehension Dataset: Description and Analysis
• Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension (EMNLP2019)
• BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer (Interspeech2019)
• Dialog State Tracking: A Neural Reading Comprehension Approach
• A Simple but Effective BERT Model for Dialog State Tracking on Resource-Limited Systems (ICASSP2020)
• Fine-Tuning BERT for Schema-Guided Zero-Shot Dialogue State Tracking
• Goal-Oriented Multi-Task BERT-Based Dialogue State Tracker
• Domain Adaptive Training BERT for Response Selection
• BERT Goes to Law School: Quantifying the Competitive Advantage of Access to Large Legal Corpora in Contract Understanding

7、机器翻译

• The Evolved Transformer - David R. So(2019)

8、综述

• Evolution of transfer learning in natural language processing
• Pre-trained Models for Natural Language Processing: A Survey
• A Survey on Contextual Embeddings

9、谓词填充

• BERT for Joint Intent Classification and Slot Filling
• Multi-lingual Intent Detection and Slot Filling in a Joint BERT-based Model
• A Comparison of Deep Learning Methods for Language Understanding (Interspeech2019)

10、实体识别

• BERT Meets Chinese Word Segmentation
• Toward Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning
• Establishing Strong Baselines for the New Decade: Sequence Tagging, Syntactic and Semantic Parsing with BERT
• Evaluating Contextualized Embeddings on 54 Languages in POS Tagging, Lemmatization and Dependency Parsing
• NEZHA: Neural Contextualized Representation for Chinese Language Understanding
• Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing -- A Tale of Two Parsers Revisited (EMNLP2019)
• Is POS Tagging Necessary or Even Helpful for Neural Dependency Parsing?
• Parsing as Pretraining (AAAI2020)
• Cross-Lingual BERT Transformation for Zero-Shot Dependency Parsing
• Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement
• Named Entity Recognition -- Is there a glass ceiling? (CoNLL2019)
• A Unified MRC Framework for Named Entity Recognition
• Training Compact Models for Low Resource Entity Tagging using Pre-trained Language Models
• Robust Named Entity Recognition with Truecasing Pretraining (AAAI2020)
• LTP: A New Active Learning Strategy for Bert-CRF Based Named Entity Recognition
• MT-BioNER: Multi-task Learning for Biomedical Named Entity Recognition using Deep Bidirectional Transformers
• Portuguese Named Entity Recognition using BERT-CRF
• Towards Lingua Franca Named Entity Recognition with BERT

11、关系抽取

• Matching the Blanks: Distributional Similarity for Relation Learning (ACL2019)
• BERT-Based Multi-Head Selection for Joint Entity-Relation Extraction (NLPCC2019)
• Enriching Pre-trained Language Model with Entity Information for Relation Classification
• Span-based Joint Entity and Relation Extraction with Transformer Pre-training
• Fine-tune Bert for DocRED with Two-step Process
• Entity, Relation, and Event Extraction with Contextualized Span Representations (EMNLP2019)

12、知识库

• KG-BERT: BERT for Knowledge Graph Completion
• Language Models as Knowledge Bases? (EMNLP2019) [github]
• BERT is Not a Knowledge Base (Yet): Factual Knowledge vs. Name-Based Reasoning in Unsupervised QA
• Inducing Relational Knowledge from BERT (AAAI2020)
• Latent Relation Language Models (AAAI2020)
• Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Model (ICLR2020)
• Zero-shot Entity Linking with Dense Entity Retrieval
• Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linking (CoNLL2019)
• Improving Entity Linking by Modeling Latent Entity Type Information (AAAI2020)
• PEL-BERT: A Joint Model for Protocol Entity Linking
• How Can We Know What Language Models Know?
• REALM: Retrieval-Augmented Language Model Pre-Training

13、文本分类

• How to Fine-Tune BERT for Text Classification?
• X-BERT: eXtreme Multi-label Text Classification with BERT
• DocBERT: BERT for Document Classification
• Enriching BERT with Knowledge Graph Embeddings for Document Classification
• Classification and Clustering of Arguments with Contextualized Word Embeddings (ACL2019)
• BERT for Evidence Retrieval and Claim Verification
• Stacked DeBERT: All Attention in Incomplete Data for Text Classification
• Cost-Sensitive BERT for Generalisable Sentence Classification with Imbalanced Data

14、文本生成

• BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model (NAACL2019 WS)
• Pretraining-Based Natural Language Generation for Text Summarization
• Text Summarization with Pretrained Encoders (EMNLP2019) [github (original)] [github (huggingface)]
• Multi-stage Pretraining for Abstractive Summarization
• PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
• MASS: Masked Sequence to Sequence Pre-training for Language Generation (ICML2019) [github], [github]
• Unified Language Model Pre-training for Natural Language Understanding and Generation [github] (NeurIPS2019)
• UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training [github]
• ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training
• Towards Making the Most of BERT in Neural Machine Translation
• Improving Neural Machine Translation with Pre-trained Representation
• On the use of BERT for Neural Machine Translation (EMNLP2019 WS)
• Incorporating BERT into Neural Machine Translation (ICLR2020)
• Recycling a Pre-trained BERT Encoder for Neural Machine Translation
• Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
• Mask-Predict: Parallel Decoding of Conditional Masked Language Models (EMNLP2019)
• BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
• ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation
• Cross-Lingual Natural Language Generation via Pre-Training (AAAI2020) [github]
• Multilingual Denoising Pre-training for Neural Machine Translation
• PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable
• Unsupervised Pre-training for Natural Language Generation: A Literature Review

15、纠错(多任务、masking策略等)

• Multi-Task Deep Neural Networks for Natural Language Understanding (ACL2019)
• The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding
• BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning (ICML2019)
• Unifying Question Answering and Text Classification via Span Extraction
• ERNIE: Enhanced Language Representation with Informative Entities (ACL2019)
• ERNIE: Enhanced Representation through Knowledge Integration
• ERNIE 2.0: A Continual Pre-training Framework for Language Understanding (AAAI2020)
• Pre-Training with Whole Word Masking for Chinese BERT
• SpanBERT: Improving Pre-training by Representing and Predicting Spans [github]
• Blank Language Models
• Efficient Training of BERT by Progressively Stacking (ICML2019) [github]
• RoBERTa: A Robustly Optimized BERT Pretraining Approach [github]
• ALBERT: A Lite BERT for Self-supervised Learning of Language Representations (ICLR2020)
• ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators (ICLR2020) [github] [blog]
• FreeLB: Enhanced Adversarial Training for Language Understanding (ICLR2020)
• KERMIT: Generative Insertion-Based Modeling for Sequences
• DisSent: Sentence Representation Learning from Explicit Discourse Relations (ACL2019)
• StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding (ICLR2020)
• Syntax-Infused Transformer and BERT models for Machine Translation and Natural Language Understanding
• SenseBERT: Driving Some Sense into BERT
• Semantics-aware BERT for Language Understanding (AAAI2020)
• K-BERT: Enabling Language Representation with Knowledge Graph
• Knowledge Enhanced Contextual Word Representations (EMNLP2019)
• KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
• Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (EMNLP2019)
• SBERT-WK: A Sentence Embedding Method By Dissecting BERT-based Word Models
• Universal Text Representation from BERT: An Empirical Study
• Symmetric Regularization based BERT for Pair-wise Semantic Reasoning
• Transfer Fine-Tuning: A BERT Case Study (EMNLP2019)
• Improving Pre-Trained Multilingual Models with Vocabulary Expansion (CoNLL2019)
• SesameBERT: Attention for Anywhere
• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer [github]
• SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization

16、多模态系列

• VideoBERT: A Joint Model for Video and Language Representation Learning (ICCV2019)
• ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks (NeurIPS2019)
• VisualBERT: A Simple and Performant Baseline for Vision and Language
• Selfie: Self-supervised Pretraining for Image Embedding
• ImageBERT: Cross-modal Pre-training with Large-scale Weak-supervised Image-Text Data
• Contrastive Bidirectional Transformer for Temporal Representation Learning
• M-BERT: Injecting Multimodal Information in the BERT Structure
• LXMERT: Learning Cross-Modality Encoder Representations from Transformers (EMNLP2019)
• Fusion of Detected Objects in Text for Visual Question Answering (EMNLP2019)
• BERT representations for Video Question Answering (WACV2020)
• Unified Vision-Language Pre-Training for Image Captioning and VQA [github]
• Large-scale Pretraining for Visual Dialog: A Simple State-of-the-Art Baseline
• VL-BERT: Pre-training of Generic Visual-Linguistic Representations (ICLR2020)
• Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training
• UNITER: Learning UNiversal Image-TExt Representations
• Supervised Multimodal Bitransformers for Classifying Images and Text
• Weak Supervision helps Emergence of Word-Object Alignment and improves Vision-Language Tasks
• BERT Can See Out of the Box: On the Cross-modal Transferability of Text Representations
• BERT for Large-scale Video Segment Classification with Test-time Augmentation (ICCV2019WS)
• SpeechBERT: Cross-Modal Pre-trained Language Model for End-to-end Spoken Question Answering
• vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations
• Effectiveness of self-supervised pre-training for speech recognition
• Understanding Semantics from Speech Through Pre-training
• Towards Transfer Learning for End-to-End Speech Synthesis from Deep Pre-Trained Language Models

17、模型压缩

• Distilling Task-Specific Knowledge from BERT into Simple Neural Networks
• Patient Knowledge Distillation for BERT Model Compression (EMNLP2019)
• Small and Practical BERT Models for Sequence Labeling (EMNLP2019)
• Pruning a BERT-based Question Answering Model
• TinyBERT: Distilling BERT for Natural Language Understanding [github]
• DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter (NeurIPS2019 WS) [github]
• Knowledge Distillation from Internal Representations (AAAI2020)
• PoWER-BERT: Accelerating BERT inference for Classification Tasks
• WaLDORf: Wasteless Language-model Distillation On Reading-comprehension
• Extreme Language Model Compression with Optimal Subwords and Shared Projections
• BERT-of-Theseus: Compressing BERT by Progressive Module Replacing
• Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning
• MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers
• Compressing Large-Scale Transformer-Based Models: A Case Study on BERT
• Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers
• MobileBERT: Task-Agnostic Compression of BERT by Progressive Knowledge Transfer
• Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT
• Q8BERT: Quantized 8Bit BERT (NeurIPS2019 WS)

参考：https://zhuanlan.zhihu.com/p/143123368