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Showing 2650 of 334 papers

TitleStatusHype
PCL: Peer-Contrastive Learning with Diverse Augmentations for Unsupervised Sentence EmbeddingsCode1
A Sentence is Worth 128 Pseudo Tokens: A Semantic-Aware Contrastive Learning Framework for Sentence EmbeddingsCode1
AMR-DA: Data Augmentation by Abstract Meaning RepresentationCode1
English Contrastive Learning Can Learn Universal Cross-lingual Sentence EmbeddingsCode1
ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence EmbeddingCode1
PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERTCode1
Fluent Response Generation for Conversational Question AnsweringCode1
Improving Contrastive Learning of Sentence Embeddings with Case-Augmented Positives and Retrieved NegativesCode1
BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision, Language, and GraphsCode1
An Attribution Method for Siamese EncodersCode1
ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation TransferCode1
An Efficient Self-Supervised Cross-View Training For Sentence EmbeddingCode1
Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning FrameworkCode1
Fast Converging Anytime Model CountingCode1
Bootstrapped Unsupervised Sentence Representation LearningCode1
Compositional Evaluation on Japanese Textual Entailment and SimilarityCode1
C-STS: Conditional Semantic Textual SimilarityCode1
DefSent: Sentence Embeddings using Definition SentencesCode1
DistilCSE: Effective Knowledge Distillation For Contrastive Sentence EmbeddingsCode1
DenoSent: A Denoising Objective for Self-Supervised Sentence Representation LearningCode1
Clustering-Aware Negative Sampling for Unsupervised Sentence RepresentationCode1
Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence EncodersCode1
Improving Contrastive Learning of Sentence Embeddings from AI FeedbackCode1
MedSTS: A Resource for Clinical Semantic Textual SimilarityCode1
Learning Intuitive Physics with Multimodal Generative ModelsCode1
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