README.md

NLP Paper

natural language processing paper list

Contents

• Bert Series
• Transformer Series
• Transfer Learning
• Text Summarization
• Sentiment Analysis
• Question Answering
• Machine Translation
• Surver paper
• Downstream task
  • QA MC Dialogue
  • Slot filling
  • Analysis
  • Word segmentation parsing NER
  • Pronoun coreference resolution
  • Word sense disambiguation
  • Sentiment analysis
  • Relation extraction
  • Knowledge base
  • Text classification
  • WSC WNLI NLI
  • Commonsense
  • Extractive summarization
  • IR
• Generation
• Quality evaluator
• Modification (multi-task, masking strategy, etc.)
• Probe
• Multi-lingual
• Other than English models
• Domain specific
• Multi-modal
• Model compression
• LLM
• Misc

Bert Series

• 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]
• Measuring and Reducing Gendered Correlations in Pre-trained Models

Transformer Series

• 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]
• Big Bird: Transformers for Longer Sequences

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]

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)

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)

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)

Machine Translation

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

Surver paper

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

Downstream task

QA MC Dialogue

• 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
• A BERT Baseline for the Natural Questions
• Wizard of Wikipedia

Slot filling

• 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)

Analysis

• Fine-grained Information Status Classification Using Discourse Context-Aware Self-Attention
• Neural Aspect and Opinion Term Extraction with Mined Rules as Weak Supervision (ACL2019)
• BERT-based Lexical Substitution (ACL2019)
• Assessing BERT’s Syntactic Abilities
• Does BERT agree? Evaluating knowledge of structure dependence through agreement relations
• Simple BERT Models for Relation Extraction and Semantic Role Labeling
• LIMIT-BERT : Linguistic Informed Multi-Task BERT
• A Simple BERT-Based Approach for Lexical Simplification
• Multi-headed Architecture Based on BERT for Grammatical Errors Correction (ACL2019 WS)
• Towards Minimal Supervision BERT-based Grammar Error Correction
• BERT-Based Arabic Social Media Author Profiling
• Sentence-Level BERT and Multi-Task Learning of Age and Gender in Social Media
• Evaluating the Factual Consistency of Abstractive Text Summarization
• NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution
• xSLUE: A Benchmark and Analysis Platform for Cross-Style Language Understanding and Evaluation
• TabFact: A Large-scale Dataset for Table-based Fact Verification
• Rapid Adaptation of BERT for Information Extraction on Domain-Specific Business Documents
• LAMBERT: Layout-Aware language Modeling using BERT for information extraction
• Keyphrase Extraction from Scholarly Articles as Sequence Labeling using Contextualized Embeddings (ECIR2020) [github]
• Keyphrase Extraction with Span-based Feature Representations
• What do you mean, BERT? Assessing BERT as a Distributional Semantics Model

Word segmentation parsing NER

• 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

Pronoun coreference resolution

• Resolving Gendered Ambiguous Pronouns with BERT (ACL2019 WS)
• Anonymized BERT: An Augmentation Approach to the Gendered Pronoun Resolution Challenge (ACL2019 WS)
• Gendered Pronoun Resolution using BERT and an extractive question answering formulation (ACL2019 WS)
• MSnet: A BERT-based Network for Gendered Pronoun Resolution (ACL2019 WS)
• Fill the GAP: Exploiting BERT for Pronoun Resolution (ACL2019 WS)
• On GAP Coreference Resolution Shared Task: Insights from the 3rd Place Solution (ACL2019 WS)
• Look Again at the Syntax: Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolution (ACL2019 WS)
• BERT Masked Language Modeling for Co-reference Resolution (ACL2019 WS)
• Coreference Resolution with Entity Equalization (ACL2019)
• BERT for Coreference Resolution: Baselines and Analysis (EMNLP2019) [github]
• WikiCREM: A Large Unsupervised Corpus for Coreference Resolution (EMNLP2019)
• Ellipsis and Coreference Resolution as Question Answering
• Coreference Resolution as Query-based Span Prediction
• Multi-task Learning Based Neural Bridging Reference Resolution

Word sense disambiguation

• GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge (EMNLP2019)
• Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word Representations (EMNLP2019)
• Using BERT for Word Sense Disambiguation
• Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguation (ACL2019)
• Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings (KONVENS2019)

Sentiment analysis

• 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

Relation extraction

• 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)

Knowledge base

• 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

Text classification

• 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

WSC WNLI NLI

• Exploring Unsupervised Pretraining and Sentence Structure Modelling for Winograd Schema Challenge
• A Surprisingly Robust Trick for the Winograd Schema Challenge
• WinoGrande: An Adversarial Winograd Schema Challenge at Scale (AAAI2020)
• Improving Natural Language Inference with a Pretrained Parser
• Adversarial NLI: A New Benchmark for Natural Language Understanding
• Adversarial Analysis of Natural Language Inference Systems (ICSC2020)
• HypoNLI: Exploring the Artificial Patterns of Hypothesis-only Bias in Natural Language Inference (LREC2020)
• Evaluating BERT for natural language inference: A case study on the CommitmentBank (EMNLP2019)

Commonsense

• CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge (NAACL2019)
• HellaSwag: Can a Machine Really Finish Your Sentence? (ACL2019) [website]
• Story Ending Prediction by Transferable BERT (IJCAI2019)
• Explain Yourself! Leveraging Language Models for Commonsense Reasoning (ACL2019)
• Align, Mask and Select: A Simple Method for Incorporating Commonsense Knowledge into Language Representation Models
• Informing Unsupervised Pretraining with External Linguistic Knowledge
• Commonsense Knowledge + BERT for Level 2 Reading Comprehension Ability Test
• BIG MOOD: Relating Transformers to Explicit Commonsense Knowledge
• Commonsense Knowledge Mining from Pretrained Models (EMNLP2019)
• KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning (EMNLP2019)
• Cracking the Contextual Commonsense Code: Understanding Commonsense Reasoning Aptitude of Deep Contextual Representations (EMNLP2019 WS)
• Do Massively Pretrained Language Models Make Better Storytellers? (CoNLL2019)
• PIQA: Reasoning about Physical Commonsense in Natural Language (AAAI2020)
• Evaluating Commonsense in Pre-trained Language Models (AAAI2020)
• Why Do Masked Neural Language Models Still Need Common Sense Knowledge?
• Do Neural Language Representations Learn Physical Commonsense? (CogSci2019)

Extractive summarization

• 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]

IR

• Passage Re-ranking with BERT
• Investigating the Successes and Failures of BERT for Passage Re-Ranking
• Understanding the Behaviors of BERT in Ranking
• Document Expansion by Query Prediction
• CEDR: Contextualized Embeddings for Document Ranking (SIGIR2019)
• Deeper Text Understanding for IR with Contextual Neural Language Modeling (SIGIR2019)
• FAQ Retrieval using Query-Question Similarity and BERT-Based Query-Answer Relevance (SIGIR2019)
• Multi-Stage Document Ranking with BERT
• REALM: Retrieval-Augmented Language Model Pre-Training
• How Much Knowledge Can You Pack Into the Parameters of a Language Model? [github]
• Dense Passage Retrieval for Open-Domain Question Answering [github]

Generation

• 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
• Improving Language Understanding by Generative Pre-Training
• Language Models are Unsupervised Multitask Learners
• Language Models are Few-Shot Learners

Quality evaluator

• BERTScore: Evaluating Text Generation with BERT (ICLR2020)
• Machine Translation Evaluation with BERT Regressor
• SumQE: a BERT-based Summary Quality Estimation Model (EMNLP2019)
• MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance (EMNLP2019) [github]
• BERT as a Teacher: Contextual Embeddings for Sequence-Level Reward

Modification (multi-task, masking strategy, etc.)

• 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

Probe

• A Structural Probe for Finding Syntax in Word Representations (NAACL2019)
• Linguistic Knowledge and Transferability of Contextual Representations (NAACL2019) [github]
• Probing What Different NLP Tasks Teach Machines about Function Word Comprehension (*SEM2019)
• BERT Rediscovers the Classical NLP Pipeline (ACL2019)
• Probing Neural Network Comprehension of Natural Language Arguments (ACL2019)
• Cracking the Contextual Commonsense Code: Understanding Commonsense Reasoning Aptitude of Deep Contextual Representations (EMNLP2019 WS)
• What do you mean, BERT? Assessing BERT as a Distributional Semantics Model
• Quantity doesn't buy quality syntax with neural language models (EMNLP2019)
• Are Pre-trained Language Models Aware of Phrases? Simple but Strong Baselines for Grammar Induction (ICLR2020)
• oLMpics -- On what Language Model Pre-training Captures
• How Much Knowledge Can You Pack Into the Parameters of a Language Model?
• What Does My QA Model Know? Devising Controlled Probes using Expert Knowledge

Multi-lingual

• Multilingual Constituency Parsing with Self-Attention and Pre-Training (ACL2019)
• Language Model Pretraining (NeurIPS2019) [github]
• 75 Languages, 1 Model: Parsing Universal Dependencies Universally (EMNLP2019) [github]
• Zero-shot Dependency Parsing with Pre-trained Multilingual Sentence Representations (EMNLP2019 WS)
• Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT (EMNLP2019)
• How multilingual is Multilingual BERT? (ACL2019)
• How Language-Neutral is Multilingual BERT?
• Is Multilingual BERT Fluent in Language Generation?
• Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks (EMNLP2019)
• BERT is Not an Interlingua and the Bias of Tokenization (EMNLP2019 WS)
• Cross-Lingual Ability of Multilingual BERT: An Empirical Study (ICLR2020)
• Multilingual Alignment of Contextual Word Representations (ICLR2020)
• On the Cross-lingual Transferability of Monolingual Representations
• Unsupervised Cross-lingual Representation Learning at Scale
• Emerging Cross-lingual Structure in Pretrained Language Models
• Can Monolingual Pretrained Models Help Cross-Lingual Classification?
• Fully Unsupervised Crosslingual Semantic Textual Similarity Metric Based on BERT for Identifying Parallel Data (CoNLL2019)
• What the [MASK]? Making Sense of Language-Specific BERT Models
• XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization

Other than English models

• CamemBERT: a Tasty French Language Model
• FlauBERT: Unsupervised Language Model Pre-training for French
• Multilingual is not enough: BERT for Finnish
• BERTje: A Dutch BERT Model
• RobBERT: a Dutch RoBERTa-based Language Model
• Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language
• AraBERT: Transformer-based Model for Arabic Language Understanding
• PhoBERT: Pre-trained language models for Vietnamese
• CLUECorpus2020: A Large-scale Chinese Corpus for Pre-training Language Model

Domain specific

• BioBERT: a pre-trained biomedical language representation model for biomedical text mining
• Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets (ACL2019 WS)
• BERT-based Ranking for Biomedical Entity Normalization
• PubMedQA: A Dataset for Biomedical Research Question Answering (EMNLP2019)
• Pre-trained Language Model for Biomedical Question Answering
• How to Pre-Train Your Model? Comparison of Different Pre-Training Models for Biomedical Question Answering
• ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission
• Publicly Available Clinical BERT Embeddings (NAACL2019 WS)
• Progress Notes Classification and Keyword Extraction using Attention-based Deep Learning Models with BERT
• SciBERT: Pretrained Contextualized Embeddings for Scientific Text [github]
• PatentBERT: Patent Classification with Fine-Tuning a pre-trained BERT Model

Multi-modal

• 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

Model compression

• 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)

LLM

• Attention Is All You Need
• Improving Language Understanding by Generative Pre-Training
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• Language Models are Unsupervised Multitask Learners
• Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
• ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
• Scaling Laws for Neural Language Models
• Language models are few-shot learners
• Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
• Evaluating Large Language Models Trained on Code
• Multitask Prompted Training Enables Zero-Shot Task Generalization
• GLaM: Efficient Scaling of Language Models with Mixture-of-Experts
• WebGPT: Browser-assisted question-answering with human feedback
• Improving language models by retrieving from trillions of tokens
• Scaling Language Models: Methods, Analysis & Insights from Training Gopher
• Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
• LaMDA: Language Models for Dialog Applications
• Solving Quantitative Reasoning Problems with Language Models
• Using Deep and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model
• Training language models to follow instructions with human feedback
• PaLM: Scaling Language Modeling with Pathways
• An empirical analysis of compute-optimal large language model training
• OPT: Open Pre-trained Transformer Language Models
• Unifying Language Learning Paradigms
• Emergent Abilities of Large Language Models
• Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
• Language Models are General-Purpose Interfaces
• Improving alignment of dialogue agents via targeted human judgements
• Scaling Instruction-Finetuned Language Models
• GLM-130B: An Open Bilingual Pre-trained Model
• Holistic Evaluation of Language Models
• BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
• Galactica: A Large Language Model for Science
• OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization
• The Flan Collection: Designing Data and Methods for Effective Instruction Tuning
• LLaMA: Open and Efficient Foundation Language Models
• Language Is Not All You Need: Aligning Perception with Language Models
• PaLM-E: An Embodied Multimodal Language Model
• GPT-4 Technical Report
• Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling
• Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision
• PaLM 2 Technical Report
• RWKV: Reinventing RNNs for the Transformer Era
• Direct Preference Optimization: Your Language Model is Secretly a Reward Model
• Llama 2: Open Foundation and Fine-Tuned Chat Models
• Mamba: Linear-Time Sequence Modeling with Selective State Spaces
• TinyLlama: An Open-Source Small Language Model

Misc

• jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models [github]
• Cloze-driven Pretraining of Self-attention Networks
• Learning and Evaluating General Linguistic Intelligence
• To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks (ACL2019 WS)
• Learning to Speak and Act in a Fantasy Text Adventure Game (EMNLP2019)
• Conditional BERT Contextual Augmentation
• Data Augmentation using Pre-trained Transformer Models
• Large Batch Optimization for Deep Learning: Training BERT in 76 minutes (ICLR2020)
• Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models (ICLR2020)
• A Mutual Information Maximization Perspective of Language Representation Learning (ICLR2020)
• Is BERT Really Robust? Natural Language Attack on Text Classification and Entailment (AAAI2020)
• Thieves on Sesame Street! Model Extraction of BERT-based APIs (ICLR2020)
• Graph-Bert: Only Attention is Needed for Learning Graph Representations
• CodeBERT: A Pre-Trained Model for Programming and Natural Languages
• Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping
• Extending Machine Language Models toward Human-Level Language Understanding
• Glyce: Glyph-vectors for Chinese Character Representations
• Back to the Future -- Sequential Alignment of Text Representations
• Improving Cuneiform Language Identification with BERT (NAACL2019 WS)
• BERT has a Moral Compass: Improvements of ethical and moral values of machines
• SMILES-BERT: Large Scale Unsupervised Pre-Training for Molecular Property Prediction (ACM-BCB2019)
• On the comparability of Pre-trained Language Models
• Transformers: State-of-the-art Natural Language Processing
• Jukebox: A Generative Model for Music
• WT5?! Training Text-to-Text Models to Explain their Predictions
• TAPAS: Weakly Supervised Table Parsing via Pre-training [github]
• TABERT: Pretraining for Joint Understanding of Textual and Tabular Data

Author

ChangWookJun / @changwookjun ([email protected])