BERT-related Papers
2020-03-03 16:36:12
This is a list of BERT-related papers. Any feedback is welcome.
Source: https://github.com/tomohideshibata/BERT-related-papers#domain-specific
Table of Contents
- Downstream task
- Generation
- Modification (multi-task, masking strategy, etc.)
- Transformer variants
- Probe
- Inside BERT
- Multi-lingual
- Other than English models
- Domain specific
- Multi-modal
- Model compression
- Misc.
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
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)
- Parsing as Pretraining (AAAI2020)
- Cross-Lingual BERT Transformation for Zero-Shot Dependency Parsing
- 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
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)
- Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text
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)
- 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
- Conditional BERT Contextual Augmentation
- Stacked DeBERT: All Attention in Incomplete Data for Text Classification
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)
- 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
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
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
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)
- 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
Transformer variants
- Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context (ACL2019) [github]
- 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]
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
Inside BERT
- 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?
- exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformers Models [github]
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)
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
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
- 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)
Misc.
- 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)
- BERTScore: Evaluating Text Generation with BERT (ICLR2020)
- Machine Translation Evaluation with BERT Regressor
- SumQE: a BERT-based Summary Quality Estimation Model (EMNLP2019)
- 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
- Evolution of transfer learning in natural language processing