Doragd Awesome Sentence Embedding Save

A curated list of research papers in Sentence Reprsentation Learning and a sts leaderboard of sentence embeddings.

Project README

Awesome-Sentence-Embedding

A curated list of research papers in Sentence Reprsentation Learning. The leaderboard of Unsupervised STS is also available.

Content

Unsupervised STS leaderboard

You can search by model name (alias) to find the corresponding paper in the Related Papers.

BERT-base

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

BERT-large

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

RoBERTa-base

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

RoBERTa-large

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

Main Track of Sentence Embeddings

Tips:
- 【Conf】Titile【Alias of the paper, etc.】
  • 【ICLR2023 withdrawn】 RankCSE: Unsupervised Sentence Representations Learning via Learning to Rank 【RankCSE】paper
  • 【ICLR2023 withdrawn】 Ranking-Enhanced Unsupervised Sentence Representation Learning 【RankEncoder】paper
  • 【ICLR2023 withdrawn】 Learning to Perturb for Contrastive Learning of Unsupervised Sentence Representations 【L2P-CSR】paper
  • 【ICLR2023 withdrawn】 miCSE: Mutual Information Contrastive Learning for Low-shot Sentence Embeddings 【miCSE】
  • 【ICLR2023】On The Inadequacy of Optimizing Alignment and Uniformity in Contrastive Learning of Sentence Representations
  • 【EMNLP2022】ConGen: Unsupervised Control and Generalization Distillation For Sentence Representation
  • 【EMNLP2022】Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework
  • 【EMNLP2022】 PCL: Peer-Contrastive Learning with Diverse Augmentations for Unsupervised Sentence Embeddings 【PCL】
  • 【EMNLP2022】Contrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence Embedding 【PromptCSE】
  • 【EMNLP2022】Sentence Representation Learning with Generative Objective rather than Contrastive Objective 【PaSeR, Unsupervised & Supervised STS】
  • 【EMNLP2022】DiffAug: Differential Data Augmentation for Contrastive Sentence Representation Learning 【DiffAug, Semi-supervised, Supervised STS】
  • 【EMNLP2022】InfoCSE: Information-aggregated Contrastive Learning of Sentence Embeddings 【InfoCSE】
  • 【EMNLP2022】 Improved Universal Sentence Embeddings with Prompt-based Contrastive Learning and Energy-based Learning 【PromCSE, Supervised STS】
  • 【EMNLP2022】 PromptBERT: Improving BERT Sentence Embeddings with Prompts 【PromptBERT】
  • 【COLING2022】 Smoothed contrastive learning for unsupervised sentence embedding 【GS-InfoNCE】
  • 【COLING2022】 ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding 【ESimCSE】
  • 【COLING2022】 An information minimization contrastive learning model for unsupervised sentence embeddings learning 【InforMin-CL】
  • 【NIPS2022】 Non-Linguistic Supervision for Contrastive Learning of Sentence Embeddings 【VisualCSE, AudioCSE, more data】
  • 【SIGIR2022】 Improving Contrastive Learning of Sentence Embeddings with Case-Augmented Positives and Retrieved Negatives 【CARDS】
  • 【AAAI2022】 Unsupervised Sentence Representation via Contrastive Learning with Mixing Negatives 【MixCSE】
  • 【ACL2022】 A contrastive framework for learning sentence representations from pairwise and triple-wise perspective in angular space 【ArcCSE】
  • 【ACL2022】 Debiased contrastive learning of unsupervised sentence representations 【DCLR】
  • 【ACL2022】 Virtual augmentation supported contrastive learning of sentence representations 【VaSCL】
  • 【ACL2022】 Scd: Self-contrastive decorrelation for sentence embeddings 【SCD】
  • 【ACL2022】 Exploring the impact of negative samples of contrastive learning: A case study of sentence embedding 【MoCoSE】
  • 【ACL2022】 A Sentence is Worth 128 Pseudo Tokens: A Semantic-Aware Contrastive Learning Framework for Sentence Embeddings 【PT-BERT】
  • 【ACL2022】 Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models 【ST5, Supervised STS】
  • 【NAACL2022】 DiffCSE：Difference-based Contrastive Learning for Sentence Embeddings 【DiffCSE】
  • 【NAACL2022】 EASE: Entity-Aware Contrastive Learning of Sentence Embedding 【EASE】
  • 【NAACL2022】 MCSE: Multimodal Contrastive Learning of Sentence Embeddings 【MCSE, more data】
  • 【ICML2022ws】 Boosting Monolingual Sentence Representation with Large-scale Parallel Translation Datasets 【BMSR, Supervised STS, more data】
  • 【Arxiv2022】 SNCSE: Contrastive Learning for Unsupervised Sentence Embedding with Soft Negative Samples 【SNCSE】
  • 【ICLR2021】 Trans-encoder: Unsupervised sentence-pair modelling through self-and mutual-distillations 【TENC, use unlabeled STS data】
  • 【ICLR2021】 Semantic re-tuning with contrastive tension 【CT-BERT】
  • 【Arxiv2021】 Whitening sentence representations for better semantics and faster retrieval 【BERT-whitening】
  • 【Arxiv2021】 DisCo: Effective Knowledge Distillation For Contrastive Learning of Sentence Embeddings 【DisCo, Supervised STS】
  • 【EMNLP2021】 Locality Preserving Sentence Encoding 【SBERT-LP, Supervised STS】
  • 【EMNLP2021】 Universal Sentence Representation Learning with Conditional Masked Language Model 【CMLM, Supervised STS】
  • 【EMNLP2021】 PAUSE: Positive and Annealed Unlabeled Sentence Embedding 【PAUSE, Supervised STS】
  • 【EMNLP2021】 SimCSE: Simple contrastive learning of sentence embeddings 【SimCSE】
  • 【EMNLP2021】 Fast, effective, and self-supervised: Transforming masked language models into universal lexical and sentence encoders 【Mirror-BERT】
  • 【EMNLP2021】 Pairwise Supervised Contrastive Learning of Sentence Representations 【PairSupCon, Supervised STS】
  • 【NAACL2021】 Disentangling Semantics and Syntax in Sentence Embeddings with Pre-trained Language Models 【ParaBART, Supervised STS】
  • 【ACL2021】 ConSERT: A contrastive framework for self-supervised sentence representation transfer 【ConSERT, use unlabeled STS data】
  • 【ACL2021】 DeCLUTR: Deep contrastive learning for unsupervised textual representations 【DeCLUTR】
  • 【ACL2021】 Bootstrapped unsupervised sentence representation learning 【BSL, Unsup & Sup】
  • 【ACL2021】 Self-guided contrastive learning for bert sentence representations 【SG-OPT】
  • 【SIGIR2021】 Dual-View Distilled BERT for Sentence Embedding 【DvBERT, Supervised STS】
  • 【EMNLP2020】 On the sentence embeddings from pre-trained language models 【BERT-flow】
  • 【EMNLP2020】 An unsupervised sentence embedding method by mutual information maximization 【IS-BERT】
  • 【TASLP2020】 SBERT-WK: A Sentence Embedding Method by Dissecting BERT-Based Word Models 【SBERT-WK, Supervised STS】
  • 【EMNLP2019】 Sentence-bert: Sentence embeddings using siamese bert-networks 【SBERT】
  • 【ICLR2018】 An efficient framework for learning sentence representations 【Quick-thought】
  • 【EMNLP2018】 Universal Sentence Encoder for English 【USE】
  • 【EMNLP2017】 Supervised learning of universal sentence representations from natural language inference data 【InferSent】
  • 【ACL2016】 Learning distributed representations of sentences from unlabelled data 【FastSent】
  • 【NIPS2015】 Skip-thought vectors 【Skip-thought】

Others Track of Sentence Embeddings

  • 【ACL2022】 Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation
  • 【NAACL2022】 Learning Dialogue Representations from Consecutive Utterances
  • 【NAACL2022】 On the Effectiveness of Sentence Encoding for Intent Detection Meta-Learning
  • 【ICASSP2022】 Integrating Dependency Tree into Self-Attention for Sentence Representation
  • 【ICASSP2022】 Pair-Level Supervised Contrastive Learning for Natural Language Inference
  • 【Arxiv2022】 Masked Autoencoders As The Unified Learners For Pre-Trained Sentence Representation
  • 【Arxiv2022】 RetroMAE: Pre-training Retrieval-oriented Transformers via Masked Auto-Encoder
  • 【EMNLP2021】 A Deep Decomposable Model for Disentangling Syntax and Semantics in Sentence Representation
  • 【EMNLP2021】 DialogueCSE: Dialogue-based Contrastive Learning of Sentence Embeddings
  • 【EMNLP2021】 WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach
  • 【EMNLP2021】 TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning
  • 【EMNLP2021】 Exploiting Twitter as Source of Large Corpora of Weakly Similar Pairs for Semantic Sentence Embeddings
  • 【ACL2021】 DefSent: Sentence Embeddings using Definition Sentences
  • 【ACL2021】 Discrete Cosine Transform as Universal Sentence Encoder
  • 【Arxiv2021】 S-SimCSE: sampled sub-networks for contrastive learning of sentence embedding
  • 【NAACL2021】 Augmented SBERT: Data augmentation method for improving bi-encoders for pairwise sentence scoring tasks
  • 【Arxiv2020】 CERT: contrastive self-supervised learning for language understanding
  • 【AAAI2020】 Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding
  • 【EMNLP2020】 Cross-Thought for Sentence Encoder Pre-training
  • 【WWW2020】 Enhanced-RCNN: An Efficient Method for Learning Sentence Similarity
  • 【ACL2020】 QuASE: Question-Answer Driven Sentence Encoding
  • 【EMNLP2019】 Efficient Sentence Embedding using Discrete Cosine Transform
  • 【EMNLP2019】 Parameter-free Sentence Embedding via Orthogonal Basis
  • 【EMNLP2019】 Incorporating Visual Semantics into Sentence Representations within a Grounded Space
  • 【NAACL2019】 Continual Learning for Sentence Representations Using Conceptors
  • 【NAACL2019】 A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence Representations
  • 【ACL2019】 Simple and Effective Paraphrastic Similarity from Parallel Translations
  • 【ACL2019】 Learning Compressed Sentence Representations for On-Device Text Processing
  • 【ACL2019】 Exploiting Invertible Decoders for Unsupervised Sentence Representation Learning

Cross-lingual Sentence Embeddings

  • 【EMNLP2022】A Multilingual Generative Transformer for Semantic Sentence Embedding
  • 【EMNLP2022】English Contrastive Learning Can Learn Universal Cross-lingual Sentence Embeddings 【mSimCSE】
  • 【ACL2022】 Language-agnostic BERT Sentence Embedding
  • 【IJCAI2022】 Unsupervised Context Aware Sentence Representation Pretraining for Multi-lingual Dense Retrieval
  • 【Arxiv2021】 Paraphrastic Representations at Scale
  • 【ACL2021】 Lightweight Cross-Lingual Sentence Representation Learning
  • 【ACL2021】 Bootstrapped Unsupervised Sentence Representation Learning
  • 【ACL2021】 Self-Guided Contrastive Learning for BERT Sentence Representations
  • 【EMNLP2021】 Fast, Effective and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders
  • 【EMNLP2021】 Aligning Cross-lingual Sentence Representations with Dual Momentum Contrast
  • 【EMNLP2021】 Cross-lingual Sentence Embedding using Multi-Task Learning
  • 【EMNLP2021】 Semantic Alignment with Calibrated Similarity for Multilingual Sentence Embedding
  • 【EMNLP2021】 Language-agnostic Representation from Multilingual Sentence Encoders for Cross-lingual Similarity Estimation
  • 【Arxiv2021】 Analyzing Zero-shot Cross-lingual Transfer in Supervised NLP Tasks
  • 【ACL2020】 Unsupervised Multilingual Sentence Embeddings for Parallel Corpus Mining
  • 【AAAI2020】 Emu: Enhancing Multilingual Sentence Embeddings with Semantic Specialization
  • 【AAAI2020】 Unsupervised Interlingual Semantic Representations from Sentence Embeddings for Zero-Shot Cross-Lingual Transfer
  • 【AAAI2020】 ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs
  • 【EMNLP2020】 Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation
  • 【EMNLP2020】 A Bilingual Generative Transformer for Semantic Sentence Embedding
  • 【EMNLP2019】 Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings
  • 【EMNLP2019】 Exploring Multilingual Syntactic Sentence Representations
  • 【IJCAI2019】 Improving Multilingual Sentence Embedding using Bi-directional Dual Encoder with Additive Margin Softmax

Cross-lingual Dense Retrieval

  • 【ICLR2023】 Modeling Sequential Sentence Relation to Improve Cross-lingual Dense Retrieval
  • 【ICLR2023】 LEXA: Language-agnostic Cross-consistency Training for Question Answering Tasks
  • 【EMNLP2022】Retrofitting Multilingual Sentence Embeddings with Abstract Meaning Representation
  • 【IJCAI2022】 Unsupervised Context Aware Sentence Representation Pretraining for Multi-lingual Dense Retrieval
  • 【NAACL2022】 EASE: Entity-Aware Contrastive Learning of Sentence Embedding
  • 【EMNLP2021】 A Simple and Effective Method To Eliminate the Self Language Bias in Multilingual Representations
  • 【EMNLP2020】 Language-Agnostic Answer Retrieval from a Multilingual Pool

Misc

  • 【ICLR2023】 Understanding The Role of Positional Encodings in Sentence Representations
  • 【ICLR2023】 Self-Consistent Learning: Cooperation between Generators and Discriminators
  • 【TMLR2022】 Unsupervised dense information retrieval with contrastive learning
  • 【NAACL2021】 Supporting Clustering with Contrastive Learning
Open Source Agenda is not affiliated with "Doragd Awesome Sentence Embedding" Project. README Source: Doragd/Awesome-Sentence-Embedding
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