model

文本摘要

名称	机构	作者	代码链接	论文	模型链接	年份	编程语言	运行环境	star	fork	引用
Extractive Summarization as Text Matching	Fudan University	Ming Zhong	查看	Extractive Summarization as Text Matching	查看	2020	Python	PyTorch	240	51	23
Text Summarization with Pretrained Encoders	University of Edinburgh	Yang Liu	查看	Text Summarization with Pretrained Encoders	查看	2019	Python 3.6	PyTorch 1.1.0	825	325	233
GSum	Carnegie Mellon University	Zi-Yi Dou	查看	GSum: A General Framework for Guided Neural Abstractive Summarization	查看	2020	None	None	14	1	1
ProphetNet	University of Science and Technology of China,Microsoft,Microsoft Research Asia,Sichuan University	Weizhen Qi	查看	ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training	查看	2020	Python	torch==1.3.0 fairseq==v0.9.0	229	44	34
Controlling the Amount of Verbatim Copying in Abstractive Summarization	University of Central Florida,Robert Bosch LLC	Kaiqiang Song	查看	Controlling the Amount of Verbatim Copying in Abstractive Summarization	查看	2020	Python 3.7	Pytorchv1.3 Pyrouge pytorch-pretrained-bert	33	7	7
PEGASUS	Imperial College London,Google Research	Jingqing Zhang	查看	PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization	查看	2020	Python	Google Cloud or gsutil	915	180	93
UNILM	Microsoft Research	Li Dong	查看	Unified Language Model Pre-training for Natural Language Understanding and Generation	查看	2019	Python	UniLM v1	1723	371	305
BART	Facebook AI	Mike Lewis	查看	BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension	查看	2019	Python	Pytorch	10.9k	2.8k	392
ALONE	Tokyo Institute of Technology,Tohoku University	Sho Takase	查看	All Word Embeddings from One Embedding	查看	2020	Python>=3.6	Pytorch>=1.4.0	17	1	1
Learning to Extract Coherent Summary via Deep Reinforcement Learning	Hong Kong University of Science and Technology,University of Massachusetts Medical School	YuxiangWu	查看	Learning to Extract Coherent Summary via Deep Reinforcement Learning	查看	2018	Null	Null	Null	Null	58
Extractive Summarization with SWAP-NET	Indian Institute of Science,School of Computing National University of Singapore	Aishwarya Jadhav	查看	Extractive Summarization with SWAP-NET:Sentences and Words from Alternating Pointer Networks	查看	2018	Null	Null	Null	Null	39

自然语言推理

名称	机构	作者	代码链接	论文	模型链接	年份	编程语言	运行环境	star	fork	引用
Transformer-XH	University of Maryland, College Park,Microsoft AI & Research	Chen Zhao	查看	Transformer-XH: Multi-Evidence Reasoning with eXtra Hop Attention	查看	2019	Python	NVIDIA apex	52	12	21
XLNet	Carnegie Mellon University,Google AI Brain Team	Zhilin Yang	查看	Generalized Autoregressive Pretraining for Language Understanding	查看	2019	Python2	TensorFlow 1.13.1	5.5k	1.1k	1906
RoBERTa	Paul G. Allen School of Computer Science & Engineering University of Washington,Facebook AI	Yinhan Liu	查看	RoBERTa: A Robustly Optimized BERT Pretraining Approach	查看	2019	Python>=3.6	PyTorch>= 1.5.NVIDIA GPUNCCL	11k	2.8k	686
BERT	Google AI Language	Jacob Devlin	查看	BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding	查看	2019	Python	TensorFlow	26.6k	7.5k	14563
SAE	JD AI Research	Ming Tu	查看	Select, Answer and Explain: Interpretable Multi-Hop Reading Comprehension over Multiple Documents	查看	2020	Python	PyTorch >= 1.1	22	3	22
DFGN	Shanghai Jiao Tong University,ByteDance AI Lab, China	Lin Qiu	查看	Dynamically Fused Graph Network for Multi-hop Reasoning	查看	2019	Python3	PyTorch0.4.1boto3	158	31	35
ALBERT	Google Research,Toyota Technological Institute at Chicago	Zhenzhong Lan	查看	ALBERT: A Lite BERT for Self-supervised Learning of Language Representations	查看	2019	Python	PyTorch	2.6k	475	1093
GPT	OpenAI	Alec Radford	查看	Improving Language Understanding by Generative Pre-Training	查看	2018	Python	ftfy==4.4.3spacy	1.6k	419	1844
MPNet	Nanjing University of Science and Technology,Microsoft Research	Kaitao Song	查看	MPNet: Masked and Permuted Pre-training for Language Understanding	查看	2020	Python	pytorch_transformers==1.0.0transformersscipysklearn	162	18	8
Longformer	Allen Institute for Artificial Intelligence	Iz Beltagy	查看	Longformer: The Long-Document Transformer	查看	2020	Python 3.7	cudatoolkit=10.0	1k	125	166
DPR	Facebook AI,University of Washington,Princeton University	Vladimir Karpukhin	查看	Dense Passage Retrieval for Open-Domain Question Answering	查看	2020	Python 3.6+	PyTorch 1.2.0+	477	84	37

Image Captioning

名称	机构	作者	代码链接	论文	模型链接	年份	编程语言	运行环境	star	fork	引用
VirTex	University of Michigan	Karan Desai	查看	VirTex: Learning Visual Representations from Textual Annotations	查看	2021	Python 3.6+	PyTorch 1.2.0+	330	31	10
Bottom-up and Top-down Attention	Australian National University,Microsoft Research,University of Adelaide,Macquarie University	Peter Anderson	查看	Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering	查看	2018	Python 3.6	Pytorch 0.4.1	115	28	1564
VC R-CNN	University of Electronic Science and Technology of China,Damo Academy, Alibaba Group,Nanyang Technological University,Singapore Management University	Tan Wang	查看	Visual Commonsense R-CNN	查看	2020	Python 3.7	Pytorch 1.0	255	37	15
AoA	School of Electronic and Computer Engineering, Peking University,Peng Cheng Laboratory,Macau University of Science and Technology	Lun Huang	查看	Attention on Attention for Image Captioning	查看	2019	Python 3.6	PyTorch 1.0	229	50	99
Improving IC	Ingenuity Labs Research Institute, Queen’s University,Department of Electrical and Computer Engineering, Queen’s University,School of Computer Science, Fudan University	Zhan Shi	查看	Improving Image Captioning with Better Use of Captions	查看	2020	Python 2.7.15	Torch 1.0.1	16	5	1
Self-Attention Network	National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences,School of Artificial Intelligence, University of Chinese Academy of Sciences,University of Science and Technology Beijing,Wuhan University	Longteng Guo	查看	Normalized and Geometry-Aware Self-Attention Network for Image Captioning	查看	2020	None	None	None	None	4
Meshed-Memory Transformer	University of Modena and Reggio Emilia	Marcella Cornia	查看	Meshed-Memory Transformer for Image Captioning	查看	2020	Python 3.6	Pytorch	194	42	39
X-Linear Attention Networks	JD AI Research, Beijing, China	Yingwei Pan	查看	X-Linear Attention Networks for Image Captioning	查看	2020	Python 3	PyTorch (>1.0)	157	20	32
Oscar	Microsoft Corporation,University of Washington	Xiujun Li	查看	Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks	查看	2020	Python	Pytorch	279	55	28

NER

名称	机构	作者	代码链接	论文	模型链接	年份	编程语言	运行环境	star	fork	引用
LUKE	Studio Ousia,RIKEN AIP,University of Washington,Nara Institute of Science and Technology,National Institute of Informatics	Ikuya Yamada	查看	LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention	查看	2020	Python	Pytorch	149	16	5
MRC Framework	Department of Computer Science and Technology, Zhejiang University,Shannon.AI	Xiaoya Li	查看	A Unified MRC Framework for Named Entity Recognition	查看	2019	Python	Pytorch	103	26	45
NER as Dependency Parsing	Queen Mary University,Google Research	Juntao Yu	查看	Named Entity Recognition as Dependency Parsing	查看	2020	Python 2	Pytorch	100	18	8

Relation Extraction

名称	机构	作者	代码链接	论文	模型链接	年份	编程语言	运行环境	star	fork	引用
Joint Entity and Relation Extraction	College of Computer Science and Technology, Zhejiang University,StatNLP Research Group, Singapore University of Technology and Design	Jue Wang	查看	Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders	查看	2020	python3	pytorch 1.4.0	61	16	None
Downstream Model Design	AI Application Research Center, Huawei Technologies, Shenzhen, China	Cheng Li	查看	Downstream Model Design of Pre-trained Language Model for Relation Extraction Task	查看	2020	Python	Pytorch	74	14	4

Event Extraction

名称	机构	作者	代码链接	论文	模型链接	年份	编程语言	运行环境	star	fork	引用
One for All	Alt Inc.,Department of Computer and Information Science, University of Oregon	Trung Minh Nguyen	查看	One for All: Neural Joint Modeling of Entities and Events	查看	2019	None	None	None	None	33

Natural Language Inference

名称	机构	作者	代码链接	论文	模型链接	年份	编程语言	运行环境	star	fork	引用
Self-Explaining Structures	Zhejiang University,Computer Center of Peking University,Peng Cheng Laboratory,Shannon.AI	Zijun Sun	查看	Self-Explaining Structures Improve NLP Models	查看	2020	Python	Pytorch	11	1	None
Conditionally Adaptive Multi-Task Learning	Polytechnique Montreal & Mila,Element AI,CIFAR AI Chair	Jonathan Pilault	查看	Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data	查看	2020	None	None	None	None	None
Exploring the Limits of Transfer Learning	Google, Mountain View, CA 94043, USA	Colin Raffel	查看	Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer	查看	2019	Python	TensorFlow	3113	421	575

Machine Reading Comprehension

名称	机构	作者	代码链接	论文	模型链接	年份	编程语言	运行环境	star	fork	引用
SpanBERT	Allen School of Computer Science & Engineering, University of Washington, Seattle, WA,Computer Science Department, Princeton University, Princeton, NJ,Allen Institute of Artificial Intelligence, Seattle,Facebook AI Research, Seattle	Mandar Joshi	查看	SpanBERT: Improving Pre-training by Representing and Predicting Spans	查看	2020	Python	Pytorch	500	95	282
Hierarchical Graph Network	Microsoft Dynamics 365 AI Research	Yuwei Fang	查看	Hierarchical Graph Network for Multi-hop Question Answering	查看	2019	Python	Pytorch	32	5	29