SOTAVerified

Semantic Textual Similarity

Semantic textual similarity deals with determining how similar two pieces of texts are. This can take the form of assigning a score from 1 to 5. Related tasks are paraphrase or duplicate identification.

Image source: Learning Semantic Textual Similarity from Conversations

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2051–2100 of 2381 papers

TitleStatusHype
ConTFV: A Contrastive Learning Framework for Table-based Fact Verification—0
Contrasting Syntagmatic and Paradigmatic Relations: Insights from Distributional Semantic Models—0
Contrastive Learning of Sentence Representations—0
Contrastive Learning Subspace for Text Clustering—0
Contrastive Semantic Similarity Learning for Image Captioning Evaluation with Intrinsic Auto-encoder—0
Contrastive Visual Semantic Pretraining Magnifies the Semantics of Natural Language Representations—0
Contrastive Word Embedding Learning for Neural Machine Translation—0
ConvFiT: Conversational Fine-Tuning of Pretrained Language Models—0
Convolutional Neural Network for Universal Sentence Embeddings—0
Convolutional neural networks for structured omics: OmicsCNN and the OmicsConv layer—0
Convolution-Enhanced Bilingual Recursive Neural Network for Bilingual Semantic Modeling—0
Coordination-Aware Dependency Parsing (Preliminary Report)—0
CORD19STS: COVID-19 Semantic Textual Similarity Dataset—0
Corpus-Based Paraphrase Detection Experiments and Review—0
Corpus+WordNet thesaurus generation for ontology enriching—0
Correcting the Common Discourse Bias in Linear Representation of Sentences using Conceptors—0
Cost-effective Deployment of BERT Models in Serverless Environment—0
Cost-effective Deployment of BERT Models in Serverless Environment—0
CPN-CORE: A Text Semantic Similarity System Infused with Opinion Knowledge—0
Creating Similarity: Lateral Thinking for Vertical Similarity Judgments—0
Cr\'eation et validation de signatures s\'emantiques : application \`a la mesure de similarit\'e s\'emantique et \`a la substitution lexicale (Creating and validating semantic signatures : application for measuring semantic similarity and lexical substitution)—0
Creative language explorations through a high-expressivity N-grams query language—0
CrosGrpsABS: Cross-Attention over Syntactic and Semantic Graphs for Aspect-Based Sentiment Analysis in a Low-Resource Language—0
Cross-Document Non-Fiction Narrative Alignment—0
Cross-Domain Language Modeling: An Empirical Investigation—0
Cross-Level Semantic Similarity for Serbian Newswire Texts—0
Cross-lingual Alignment of Knowledge Graph Triples with Sentences—0
Cross-lingual Learning of Semantic Textual Similarity with Multilingual Word Representations—0
Cross-Lingual News Event Correlation for Stock Market Trend Prediction—0
Cross-lingual paraphrase identification—0
Cross-Lingual Predicate Mapping Between Linked Data Ontologies—0
Cross-Lingual Question Answering Using Common Semantic Space—0
Cross-Lingual Semantic Similarity of Words as the Similarity of Their Semantic Word Responses—0
Cross-lingual Semantic Specialization via Lexical Relation Induction—0
Cross-lingual Sentence Embedding using Multi-Task Learning—0
Cross Lingual Sentiment Analysis using Modified BRAE—0
Cross-Lingual Syntactically Informed Distributed Word Representations—0
Cross-lingual Transfer or Machine Translation? On Data Augmentation for Monolingual Semantic Textual Similarity—0
Cross-Media Scientific Research Achievements Retrieval Based on Deep Language Model—0
Cross-media Similarity Metric Learning with Unified Deep Networks—0
Cross-modal Deep Metric Learning with Multi-task Regularization—0
Cross-modal Subspace Learning via Kernel Correlation Maximization and Discriminative Structure Preserving—0
Cyc3D: Fine-grained Controllable 3D Generation via Cycle Consistency Regularization—0
Czech Dataset for Semantic Similarity and Relatedness—0
Czech News Dataset for Semantic Textual Similarity—0
D2CSE: Difference-aware Deep continuous prompts for Contrastive Sentence Embeddings—0
DalGTM at SemEval-2016 Task 1: Importance-Aware Compositional Approach to Short Text Similarity—0
DALR: Dual-level Alignment Learning for Multimodal Sentence Representation Learning—0
Data Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase—0
Data Driven Content Creation using Statistical and Natural Language Processing Techniques for Financial Domain—0
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All datasetsSTS BenchmarkMTEBMRPCSICKSTS13STS14STS12STS15STS16MRPC DevSentEvalSICK-R

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SMARTRoBERTaDev Pearson Correlation92.8—Unverified
2DeBERTa (large)Accuracy92.5—Unverified
3SMART-BERTDev Pearson Correlation90—Unverified
4MT-DNN-SMARTPearson Correlation0.93—Unverified
5StructBERTRoBERTa ensemblePearson Correlation0.93—Unverified
6Mnet-SimPearson Correlation0.93—Unverified
7XLNet (single model)Pearson Correlation0.93—Unverified
8ALBERTPearson Correlation0.93—Unverified
9T5-11BPearson Correlation0.93—Unverified
10RoBERTaPearson Correlation0.92—Unverified
#ModelMetricClaimedVerifiedStatus
1AnglE-UAESpearman Correlation84.54—Unverified
2ST5-XXLSpearman Correlation82.63—Unverified
3ST5-LargeSpearman Correlation81.83—Unverified
4ST5-XLSpearman Correlation81.66—Unverified
5ST5-BaseSpearman Correlation81.14—Unverified
6MPNet-multilingualSpearman Correlation80.73—Unverified
7SGPT-5.8B-nliSpearman Correlation80.53—Unverified
8MPNetSpearman Correlation80.28—Unverified
9MiniLM-L12Spearman Correlation79.8—Unverified
10SimCSE-BERT-supSpearman Correlation79.12—Unverified
#ModelMetricClaimedVerifiedStatus
1MT-DNN-SMARTAccuracy93.7—Unverified
2ALBERTAccuracy93.4—Unverified
3RoBERTa (ensemble)Accuracy92.3—Unverified
4BigBirdF191.5—Unverified
5StructBERTRoBERTa ensembleAccuracy91.5—Unverified
6FLOATER-largeAccuracy91.4—Unverified
7SMARTAccuracy91.3—Unverified
8RoBERTa-large 355M (MLP quantized vector-wise, fine-tuned)Accuracy91—Unverified
9RoBERTa-large 355M + Entailment as Few-shot LearnerF191—Unverified
10SpanBERTAccuracy90.9—Unverified
#ModelMetricClaimedVerifiedStatus
1PromCSE-RoBERTa-large (0.355B)Spearman Correlation0.82—Unverified
2PromptEOL+CSE+LLaMA-30BSpearman Correlation0.82—Unverified
3PromptEOL+CSE+OPT-13BSpearman Correlation0.82—Unverified
4SimCSE-RoBERTalargeSpearman Correlation0.82—Unverified
5PromptEOL+CSE+OPT-2.7BSpearman Correlation0.81—Unverified
6SentenceBERTSpearman Correlation0.75—Unverified
7SRoBERTa-NLI-baseSpearman Correlation0.74—Unverified
8SRoBERTa-NLI-largeSpearman Correlation0.74—Unverified
9Dino (STS/̄🦕)Spearman Correlation0.74—Unverified
10SBERT-NLI-largeSpearman Correlation0.74—Unverified
#ModelMetricClaimedVerifiedStatus
1AnglE-LLaMA-7BSpearman Correlation0.91—Unverified
2AnglE-LLaMA-7B-v2Spearman Correlation0.91—Unverified
3PromptEOL+CSE+LLaMA-30BSpearman Correlation0.9—Unverified
4PromptEOL+CSE+OPT-13BSpearman Correlation0.9—Unverified
5PromptEOL+CSE+OPT-2.7BSpearman Correlation0.9—Unverified
6PromCSE-RoBERTa-large (0.355B)Spearman Correlation0.89—Unverified
7Trans-Encoder-BERT-large-bi (unsup.)Spearman Correlation0.89—Unverified
8Trans-Encoder-BERT-large-cross (unsup.)Spearman Correlation0.88—Unverified
9Trans-Encoder-RoBERTa-large-cross (unsup.)Spearman Correlation0.88—Unverified
10SimCSE-RoBERTa-largeSpearman Correlation0.87—Unverified