A curated list of research papers in Sentence Reprsentation Learning.
The leaderboard of Unsupervised STS is also available.
You can search by model name (alias) to find the corresponding paper in the Related Papers .
Methods
STS12
STS13
STS14
STS15
STS16
STS-B
SICK-R
avg.
GloVe(avg.)
55.14
70.66
59.73
68.25
63.66
58.02
53.76
61.32
USE
64.49
67.8
64.61
76.83
73.18
74.92
76.69
71.22
CLS
21.54
32.11
21.28
37.89
44.24
20.3
42.42
31.4
Mean
30.87
59.89
47.73
60.29
63.73
47.29
58.22
52.57
first-last avg.
39.7
59.38
49.67
66.03
66.19
53.87
62.06
56.7
Contrastive(BT)
54.26
64.03
54.28
68.19
67.5
63.27
66.91
62.63
BERT-Flow
58.4
67.1
60.85
75.16
71.22
68.66
64.47
66.55
BERT-Whitening
57.83
66.9
60.9
75.08
71.31
68.24
63.73
66.28
IS-BERT
56.77
69.24
61.21
75.23
70.16
69.21
64.25
66.58
BSL
67.83
71.4
66.88
79.97
73.97
73.74
70.4
72.03
CT-BERT
61.63
76.8
68.47
77.5
76.48
74.31
69.19
72.05
ConSERT
64.64
78.49
69.07
79.72
75.95
73.97
67.31
72.74
SCD
66.94
78.03
69.89
78.73
76.23
76.3
73.18
74.19
SG-OPT
66.84
80.13
71.23
81.56
77.17
77.23
68.16
74.62
Mirror-BERT
69.1
81.1
73
81.9
75.7
78
69.1
75.4
PaSeR
70.21
83.88
73.06
83.87
77.60
79.19
65.31
76.16
SimCSE
68.4
82.41
74.38
80.91
78.56
76.85
72.23
76.25
MCSE+coco
71.2±1.3
79.7±0.9
73.8±0.9
83.0±0.4
77.8±0.9
78.5±0.4
72.1±1.4
76.6±0.5
EASE
72.8
81.8
73.7
82.3
79.5
78.9
69.7
77
L2P-CSR
70.21
83.25
75.42
82.34
78.75
77.8
72.65
77.2
DCLR
70.81
83.73
75.11
82.56
78.44
78.31
71.59
77.22
MoCoSE
71.58
81.4
74.47
83.45
78.99
78.68
72.44
77.27
InforMin-CL
70.22
83.48
75.51
81.72
79.88
79.27
71.03
77.3
MCSE+flickr
71.4±0.9
81.8±1.3
74.8±0.9
83.6±0.9
77.5±0.8
79.5±0.5
72.6±1.4
77.3±0.5
VisualCSE
71.16
83.29
75.13
81.59
80.05
80.03
71.23
77.5
SimCSE+GS-InfoNCE
70.12
82.57
75.21
82.89
80.23
79.7
72.7
77.63
MixCSE
71.71±4.04
83.14±0.72
75.49±1.25
83.64±2.32
79.00±0.16
78.48±0.82
72.19±0.46
77.66±0.61
PT-BERT
71.2
83.76
76.34
82.63
78.9
79.42
71.94
77.74
AudioCSE
71.65
84.27
76.69
83.22
78.69
79.94
70.49
77.85
ArcCSE
72.08
84.27
76.25
82.32
79.54
79.92
72.39
78.11
miCSE
71.71
83.09
75.46
83.13
80.22
79.7
73.62
78.13
PCL
72.74
83.36
76.05
83.07
79.26
79.72
72.75
78.14
ESimCSE
73.4
83.27
77.25
82.66
78.81
80.17
72.3
78.27
DiffCSE
72.28
84.43
76.47
83.9
80.54
80.59
71.23
78.49
PromptBERT
71.56
84.58
76.98
84.47
80.6
81.6
69.87
78.54
InfoCSE
70.53
84.59
76.40
85.10
81.95
82.00
71.37
78.85
SNCSE
70.67
84.79
76.99
83.69
80.51
81.35
74.77
78.97
Prompt+L2P-CSR
72.34
84.81
78.13
84.16
80.58
82.04
71.13
79.03
RankCSE_listNet
74.38
85.97
77.51
84.46
81.31
81.46
75.26
80.05
RankEncoder
74.88
85.59
78.61
83.5
80.56
81.55
75.78
80.07
RankCSE_listMLE
75.66
86.27
77.81
84.74
81.1
81.8
75.13
80.36
Methods
STS12
STS13
STS14
STS15
STS16
STS-B
SICK-R
avg.
CLS
27.44
30.76
22.59
29.98
42.74
26.75
43.44
31.96
Mean
27.67
55.79
44.49
51.67
61.88
47
53.85
48.9
Contrastive(BT)
52.04
62.59
54.25
71.07
66.71
63.84
66.53
62.43
BERT-Flow
62.82
71.24
65.39
78.98
73.23
72.72
63.77
70.07
BERT-Whitening
64.34
74.6
69.64
74.68
75.9
72.48
60.8
70.35
SG-OPT
67.02
79.42
70.38
81.72
76.35
76.16
70.2
74.46
ConSERT
70.69
82.96
74.13
82.78
76.66
77.53
70.37
76.45
SimCSE
70.88
84.16
76.43
84.5
79.76
79.26
73.88
78.41
MixCSE
72.55±0.49
84.32±0.53
76.69±0.76
84.31±0.10
79.67±0.28
79.90±0.18
74.07±0.13
78.80±0.09
DCLR
71.87
84.83
77.37
84.7
79.81
79.55
74.19
78.9
L2P-CSR
71.44
85.09
76.88
84.71
80
79.75
74.55
78.92
SimCSE+GS-InfoNCE
73.75
85.09
77.35
84.44
79.88
79.94
73.48
78.96
MoCoSE
74.5
84.54
77.32
84.11
79.67
80.53
73.26
79.13
ESimCSE
73.21
85.37
77.73
84.3
78.92
80.73
74.89
79.31
ArcCSE
73.17
86.19
77.9
84.97
79.43
80.45
73.5
79.37
PromptBERT
73.29
86.39
77.9
85.18
79.97
81.92
71.26
79.42
Prompt+L2P-CSR
73.14
86.78
78.67
85.77
80.32
82.23
72.57
79.93
PCL
74.89
85.88
78.33
85.3
80.13
81.39
73.66
79.94
InfoCSE
71.89
86.17
77.72
86.20
81.29
83.16
74.84
80.18
SNCSE
71.94
86.66
78.84
85.74
80.72
82.29
75.11
80.19
RankCSE_listNet
74.75
86.46
78.52
85.41
80.62
81.4
76.12
80.47
RankCSE_listMLE
75.48
86.5
78.6
85.45
81.09
81.58
75.53
80.6
Methods
STS12
STS13
STS14
STS15
STS16
STS-B
SICK-R
avg.
CLS
16.67
45.57
30.36
55.08
56.98
45.41
61.89
44.57
Mean
32.11
56.33
45.22
61.34
61.98
54.53
62.03
53.36
BERT-Whitening
46.99
63.24
57.23
71.36
68.99
61.36
62.91
61.73
DeCLUTR
52.41
75.19
65.52
77.12
78.63
72.41
68.62
69.99
BSL
68.47
72.41
68.48
78.5
72.77
78.77
69.97
72.76
SG-OPT
62.57
78.96
69.24
79.99
77.17
77.6
68.42
73.42
SCD
63.53
77.79
69.79
80.21
77.29
76.55
72.1
73.89
Contrastive(BT)
62.34
78.6
68.65
79.31
77.49
79.93
71.97
74.04
Mirror-BERT
66.6
82.7
74
82.4
79.7
79.6
69.7
76.4
SimCSE
70.16
81.77
73.24
81.36
80.65
80.22
68.56
76.57
EASE
70.9
81.5
73.5
82.6
80.5
80
68.4
76.8
VaSCL
69.02
82.38
73.93
82.54
80.96
69.4
80.52
76.96
InforMin-CL
69.79
82.57
73.36
80.91
81.28
81.07
70.3
77.04
ESimCSE
69.9
82.5
74.68
83.19
80.3
80.99
70.54
77.44
MCSE+coco
70.2±1.7
82.0±0.7
75.5±1.2
83.0±0.6
81.5±0.7
80.8±1.0
69.9±0.6
77.6±0.8
SimCSE+GS-InfoNCE
71.12
83.24
75
82.61
81.36
81.26
69.62
77.74
L2P-CSR
71.69
82.43
74.55
82.15
81.81
81.36
70.22
77.74
AudioCSE
68.44
83.96
75.77
82.38
82.07
81.63
70.56
77.83
DCLR
70.01
83.08
75.09
83.66
81.06
81.86
70.33
77.87
VisualCSE
70.41
83.51
74.87
82.79
81.67
81.89
69.95
77.87
PCL
71.54
82.7
75.38
83.31
81.64
81.61
69.19
77.91
DiffCSE
70.05
83.43
75.49
82.81
82.12
82.38
71.19
78.21
MCSE+flickr
71.7±0.2
82.7±0.4
75.9±0.3
84.0±0.4
81.3±0.3
82.3±0.5
70.3±1.3
78.3±0.1
CARDS
72.49
84.09
76.19
82.98
82.11
82.25
70.65
78.68
PromptBERT
73.94
84.74
77.28
84.99
81.74
81.88
69.5
79.15
SNCSE
70.62
84.42
77.24
84.85
81.49
83.07
72.92
79.23
RankCSE_listMLE
72.74
84.24
75.99
84.68
82.88
83.16
71.77
79.35
RankCSE_listNet
72.88
84.5
76.46
84.67
83
83.24
71.67
79.49
Prompt+L2P-CSR
74.97
83.63
78.28
84.86
82.03
82.77
71.26
79.69
Methods
STS12
STS13
STS14
STS15
STS16
STS-B
SICK-R
avg.
CLS
19.25
22.97
14.93
33.41
38.01
12.52
40.63
25.96
Mean
33.63
57.22
45.67
63
61.18
47.07
58.38
52.31
Contrastive(BT)
57.6
72.14
62.25
71.49
71.75
77.05
67.83
68.59
BERT-Whitening
64.17
73.92
71.06
76.4
74.87
71.68
58.49
70.08
SG-OPT
64.29
76.36
68.48
80.1
76.6
78.14
67.97
73.13
InforMin-CL
70.91
84.2
75.57
82.26
79.68
81.1
72.81
78.08
SimCSE
72.86
83.99
75.62
84.77
81.8
81.98
71.26
78.9
VaSCL
73.36
83.55
77.16
83.25
80.66
72.96
82.36
79.04
SimCSE+GS-InfoNCE
71.76
84.91
76.79
84.35
81.74
82.97
71.71
79.21
DCLR
73.09
84.57
76.13
85.15
81.99
82.35
71.8
79.3
PCL
73.76
84.59
76.81
85.37
81.66
82.89
70.33
79.34
PromptBERT
73.24
83.08
77.97
84.03
81.57
82.85
73.28
79.43
ESimCSE
73.2
84.93
76.88
84.86
81.21
82.79
72.27
79.45
AudioCSE
72.1
84.3
76.74
85.11
82.51
82.94
72.45
79.45
L2P-CSR
73.29
84.08
76.65
85.47
82.7
82.15
72.36
79.53
VisualCSE
73.09
84.77
77.09
85.47
82.06
83.26
72.23
79.71
Prompt+L2P-CSR
73.65
84.08
78.29
85.36
82.15
83.7
73.47
80.1
RankCSE_listMLE
73.4
85.34
77.25
85.45
82.64
84.14
72.92
80.16
RankCSE_listNet
73.23
85.08
77.5
85.67
82.99
84.2
72.98
80.24
CARDS
74.63
86.27
79.25
85.93
83.17
83.86
72.77
80.84
SNCSE
73.71
86.73
80.35
86.8
83.06
84.31
77.43
81.77
Tips:
- ăConfăTitileăAlias of the paper, etc.ă
ăICLR2023 withdrawnă RankCSE: Unsupervised Sentence Representations Learning via Learning to Rank ăRankCSEăpaper
ăICLR2023 withdrawnă Ranking-Enhanced Unsupervised Sentence Representation Learning ăRankEncoderăpaper
ăICLR2023 withdrawnă Learning to Perturb for Contrastive Learning of Unsupervised Sentence Representations ăL2P-CSRăpaper
ăICLR2023 withdrawnă miCSE: Mutual Information Contrastive Learning for Low-shot Sentence Embeddings ămiCSEă
ăICLR2023ăOn The Inadequacy of Optimizing Alignment and Uniformity in Contrastive Learning of Sentence Representations
ăEMNLP2022ăConGen: Unsupervised Control and Generalization Distillation For Sentence Representation
ăEMNLP2022ăGenerate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework
ăEMNLP2022ă PCL: Peer-Contrastive Learning with Diverse Augmentations for Unsupervised Sentence Embeddings ăPCLă
ăEMNLP2022ăContrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence Embedding ăPromptCSEă
ăEMNLP2022ăSentence Representation Learning with Generative Objective rather than Contrastive Objective ăPaSeR, Unsupervised & Supervised STSă
ăEMNLP2022ăDiffAug: Differential Data Augmentation for Contrastive Sentence Representation Learning ăDiffAug, Semi-supervised, Supervised STSă
ăEMNLP2022ăInfoCSE: Information-aggregated Contrastive Learning of Sentence Embeddings ăInfoCSEă
ăEMNLP2022ă Improved Universal Sentence Embeddings with Prompt-based Contrastive Learning and Energy-based Learning ăPromCSE, Supervised STSă
ăEMNLP2022ă PromptBERT: Improving BERT Sentence Embeddings with Prompts ăPromptBERTă
ăCOLING2022ă Smoothed contrastive learning for unsupervised sentence embedding ăGS-InfoNCEă
ăCOLING2022ă ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding ăESimCSEă
ăCOLING2022ă An information minimization contrastive learning model for unsupervised sentence embeddings learning ăInforMin-CLă
ăNIPS2022ă Non-Linguistic Supervision for Contrastive Learning of Sentence Embeddings ăVisualCSE, AudioCSE, more dataă
ăSIGIR2022ă Improving Contrastive Learning of Sentence Embeddings with Case-Augmented Positives and Retrieved Negatives ăCARDSă
ăAAAI2022ă Unsupervised Sentence Representation via Contrastive Learning with Mixing Negatives ăMixCSEă
ăACL2022ă A contrastive framework for learning sentence representations from pairwise and triple-wise perspective in angular space ăArcCSEă
ăACL2022ă Debiased contrastive learning of unsupervised sentence representations ăDCLRă
ăACL2022ă Virtual augmentation supported contrastive learning of sentence representations ăVaSCLă
ăACL2022ă Scd: Self-contrastive decorrelation for sentence embeddings ăSCDă
ăACL2022ă Exploring the impact of negative samples of contrastive learning: A case study of sentence embedding ăMoCoSEă
ăACL2022ă A Sentence is Worth 128 Pseudo Tokens: A Semantic-Aware Contrastive Learning Framework for Sentence Embeddings ăPT-BERTă
ăACL2022ă Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models ăST5, Supervised STSă
ăNAACL2022ă DiffCSEïŒDifference-based Contrastive Learning for Sentence Embeddings ăDiffCSEă
ăNAACL2022ă EASE: Entity-Aware Contrastive Learning of Sentence Embedding ăEASEă
ăNAACL2022ă MCSE: Multimodal Contrastive Learning of Sentence Embeddings ăMCSE, more dataă
ăICML2022wsă Boosting Monolingual Sentence Representation with Large-scale Parallel Translation Datasets ăBMSR, Supervised STS, more dataă
ăArxiv2022ă SNCSE: Contrastive Learning for Unsupervised Sentence Embedding with Soft Negative Samples ăSNCSEă
ăICLR2021ă Trans-encoder: Unsupervised sentence-pair modelling through self-and mutual-distillations ăTENC, use unlabeled STS dataă
ăICLR2021ă Semantic re-tuning with contrastive tension ăCT-BERTă
ăArxiv2021ă Whitening sentence representations for better semantics and faster retrieval ăBERT-whiteningă
ăArxiv2021ă DisCo: Effective Knowledge Distillation For Contrastive Learning of Sentence Embeddings ăDisCo, Supervised STSă
ăEMNLP2021ă Locality Preserving Sentence Encoding ăSBERT-LP, Supervised STSă
ăEMNLP2021ă Universal Sentence Representation Learning with Conditional Masked Language Model ăCMLM, Supervised STSă
ăEMNLP2021ă PAUSE: Positive and Annealed Unlabeled Sentence Embedding ăPAUSE, Supervised STSă
ăEMNLP2021ă SimCSE: Simple contrastive learning of sentence embeddings ăSimCSEă
ăEMNLP2021ă Fast, effective, and self-supervised: Transforming masked language models into universal lexical and sentence encoders ăMirror-BERTă
ăEMNLP2021ă Pairwise Supervised Contrastive Learning of Sentence Representations ăPairSupCon, Supervised STSă
ăNAACL2021ă Disentangling Semantics and Syntax in Sentence Embeddings with Pre-trained Language Models ăParaBART, Supervised STSă
ăACL2021ă ConSERT: A contrastive framework for self-supervised sentence representation transfer ăConSERT, use unlabeled STS dataă
ăACL2021ă DeCLUTR: Deep contrastive learning for unsupervised textual representations ăDeCLUTRă
ăACL2021ă Bootstrapped unsupervised sentence representation learning ăBSL, Unsup & Supă
ăACL2021ă Self-guided contrastive learning for bert sentence representations ăSG-OPTă
ăSIGIR2021ă Dual-View Distilled BERT for Sentence Embedding ăDvBERT, Supervised STSă
ăEMNLP2020ă On the sentence embeddings from pre-trained language models ăBERT-flowă
ăEMNLP2020ă An unsupervised sentence embedding method by mutual information maximization ăIS-BERTă
ăTASLP2020ă SBERT-WK: A Sentence Embedding Method by Dissecting BERT-Based Word Models ăSBERT-WK, Supervised STSă
ăEMNLP2019ă Sentence-bert: Sentence embeddings using siamese bert-networks ăSBERTă
ăICLR2018ă An efficient framework for learning sentence representations ăQuick-thoughtă
ăEMNLP2018ă Universal Sentence Encoder for English ăUSEă
ăEMNLP2017ă Supervised learning of universal sentence representations from natural language inference data ăInferSentă
ăACL2016ă Learning distributed representations of sentences from unlabelled data ăFastSentă
ăNIPS2015ă Skip-thought vectors ăSkip-thoughtă
ăACL2022ă Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation
ăNAACL2022ă Learning Dialogue Representations from Consecutive Utterances
ăNAACL2022ă On the Effectiveness of Sentence Encoding for Intent Detection Meta-Learning
ăICASSP2022ă Integrating Dependency Tree into Self-Attention for Sentence Representation
ăICASSP2022ă Pair-Level Supervised Contrastive Learning for Natural Language Inference
ăArxiv2022ă Masked Autoencoders As The Unified Learners For Pre-Trained Sentence Representation
ăArxiv2022ă RetroMAE: Pre-training Retrieval-oriented Transformers via Masked Auto-Encoder
ăEMNLP2021ă A Deep Decomposable Model for Disentangling Syntax and Semantics in Sentence Representation
ăEMNLP2021ă DialogueCSE: Dialogue-based Contrastive Learning of Sentence Embeddings
ăEMNLP2021ă WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach
ăEMNLP2021ă TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning
ăEMNLP2021ă Exploiting Twitter as Source of Large Corpora of Weakly Similar Pairs for Semantic Sentence Embeddings
ăACL2021ă DefSent: Sentence Embeddings using Definition Sentences
ăACL2021ă Discrete Cosine Transform as Universal Sentence Encoder
ăArxiv2021ă S-SimCSE: sampled sub-networks for contrastive learning of sentence embedding
ăNAACL2021ă Augmented SBERT: Data augmentation method for improving bi-encoders for pairwise sentence scoring tasks
ăArxiv2020ă CERT: contrastive self-supervised learning for language understanding
ăAAAI2020ă Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding
ăEMNLP2020ă Cross-Thought for Sentence Encoder Pre-training
ăWWW2020ă Enhanced-RCNN: An Efficient Method for Learning Sentence Similarity
ăACL2020ă QuASE: Question-Answer Driven Sentence Encoding
ăEMNLP2019ă Efficient Sentence Embedding using Discrete Cosine Transform
ăEMNLP2019ă Parameter-free Sentence Embedding via Orthogonal Basis
ăEMNLP2019ă Incorporating Visual Semantics into Sentence Representations within a Grounded Space
ăNAACL2019ă Continual Learning for Sentence Representations Using Conceptors
ăNAACL2019ă A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence Representations
ăACL2019ă Simple and Effective Paraphrastic Similarity from Parallel Translations
ăACL2019ă Learning Compressed Sentence Representations for On-Device Text Processing
ăACL2019ă Exploiting Invertible Decoders for Unsupervised Sentence Representation Learning
ăEMNLP2022ăA Multilingual Generative Transformer for Semantic Sentence Embedding
ăEMNLP2022ăEnglish Contrastive Learning Can Learn Universal Cross-lingual Sentence Embeddings ămSimCSEă
ăACL2022ă Language-agnostic BERT Sentence Embedding
ăIJCAI2022ă Unsupervised Context Aware Sentence Representation Pretraining for Multi-lingual Dense Retrieval
ăArxiv2021ă Paraphrastic Representations at Scale
ăACL2021ă Lightweight Cross-Lingual Sentence Representation Learning
ăACL2021ă Bootstrapped Unsupervised Sentence Representation Learning
ăACL2021ă Self-Guided Contrastive Learning for BERT Sentence Representations
ăEMNLP2021ă Fast, Effective and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders
ăEMNLP2021ă Aligning Cross-lingual Sentence Representations with Dual Momentum Contrast
ăEMNLP2021ă Cross-lingual Sentence Embedding using Multi-Task Learning
ăEMNLP2021ă Semantic Alignment with Calibrated Similarity for Multilingual Sentence Embedding
ăEMNLP2021ă Language-agnostic Representation from Multilingual Sentence Encoders for Cross-lingual Similarity Estimation
ăArxiv2021ă Analyzing Zero-shot Cross-lingual Transfer in Supervised NLP Tasks
ăACL2020ă Unsupervised Multilingual Sentence Embeddings for Parallel Corpus Mining
ăAAAI2020ă Emu: Enhancing Multilingual Sentence Embeddings with Semantic Specialization
ăAAAI2020ă Unsupervised Interlingual Semantic Representations from Sentence Embeddings for Zero-Shot Cross-Lingual Transfer
ăAAAI2020ă ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs
ăEMNLP2020ă Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation
ăEMNLP2020ă A Bilingual Generative Transformer for Semantic Sentence Embedding
ăEMNLP2019ă Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings
ăEMNLP2019ă Exploring Multilingual Syntactic Sentence Representations
ăIJCAI2019ă Improving Multilingual Sentence Embedding using Bi-directional Dual Encoder with Additive Margin Softmax
ăICLR2023ă Modeling Sequential Sentence Relation to Improve Cross-lingual Dense Retrieval
ăICLR2023ă LEXA: Language-agnostic Cross-consistency Training for Question Answering Tasks
ăEMNLP2022ăRetrofitting Multilingual Sentence Embeddings with Abstract Meaning Representation
ăIJCAI2022ă Unsupervised Context Aware Sentence Representation Pretraining for Multi-lingual Dense Retrieval
ăNAACL2022ă EASE: Entity-Aware Contrastive Learning of Sentence Embedding
ăEMNLP2021ă A Simple and Effective Method To Eliminate the Self Language Bias in Multilingual Representations
ăEMNLP2020ă Language-Agnostic Answer Retrieval from a Multilingual Pool
ăICLR2023ă Understanding The Role of Positional Encodings in Sentence Representations
ăICLR2023ă Self-Consistent Learning: Cooperation between Generators and Discriminators
ăTMLR2022ă Unsupervised dense information retrieval with contrastive learning
ăNAACL2021ă Supporting Clustering with Contrastive Learning
