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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

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Showing 1301–1350 of 2381 papers

TitleStatusHype
Semantic Measures for the Comparison of Units of Language, Concepts or Instances from Text and Knowledge Base Analysis—0
Semantic Mirror Jailbreak: Genetic Algorithm Based Jailbreak Prompts Against Open-source LLMs—0
Semantic Oppositeness Assisted Deep Contextual Modeling for Automatic Rumor Detection in Social Networks—0
Semantic Parsing for Single-Relation Question Answering—0
Semantic Pose Verification for Outdoor Visual Localization with Self-supervised Contrastive Learning—0
Semantic relation clustering for unsupervised information extraction (Regroupement s\'emantique de relations pour l'extraction d'information non supervis\'ee) [in French]—0
Semantic Relations between Events and their Time, Locations and Participants for Event Coreference Resolution—0
Semantic Role Labeling with Neural Network Factors—0
Semantic sentence similarity: size does not always matter—0
Semantic Similarity Analysis for Paraphrase Identification in Arabic Texts—0
Knowledge-Based Construction of Confusion Matrices for Multi-Label Classification Algorithms using Semantic Similarity Measures—0
Semantic Similarity-Based Clustering of Findings From Security Testing Tools—0
SDM-Net: A Simple and Effective Model for Generalized Zero-Shot Learning—0
Semantic Similarity Computation for Abstract and Concrete Nouns Using Network-based Distributional Semantic Models—0
Semantic Similarity Computing for Scientific Academic Conferences fused with domain features—0
Semantic Similarity Computing Model Based on Multi Model Fine-Grained Nonlinear Fusion—0
Semantic similarity estimation for domain specific data using BERT and other techniques—0
Semantic Similarity from Natural Language and Ontology Analysis—0
Semantic Similarity Matching for Patent Documents Using Ensemble BERT-related Model and Novel Text Processing Method—0
Semantic Similarity Measure of Natural Language Text through Machine Learning and a Keyword-Aware Cross-Encoder-Ranking Summarizer -- A Case Study Using UCGIS GIS&T Body of Knowledge—0
Semantic Similarity Measures Applied to an Ontology for Human-Like Interaction—0
Semantic Similarity Models for Depression Severity Estimation—0
Semantic Similarity of Arabic Sentences with Word Embeddings—0
Semantic Similarity Score for Measuring Visual Similarity at Semantic Level—0
Semantic Similarity Strategies for Job Title Classification—0
Semantic Similarity To Improve Question Understanding in a Virtual Patient—0
Semantics in Multi-objective Genetic Programming—0
Semantic Specialization of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints—0
Semantic Stealth: Adversarial Text Attacks on NLP Using Several Methods—0
Semantic Text Transmission via Prediction with Small Language Models: Cost-Similarity Trade-off—0
Semantic Textual Similarity for MT evaluation—0
Semantic Textual Similarity in Quality Estimation—0
Semantic Textual Similarity: past present and future—0
Semantic Textual Similarity with Siamese Neural Networks—0
Semantic Word Clusters Using Signed Spectral Clustering—0
SemantiKLUE: Robust Semantic Similarity at Multiple Levels Using Maximum Weight Matching—0
SemantiKLUE: Semantic Textual Similarity with Maximum Weight Matching—0
SemCSE: Semantic Contrastive Sentence Embeddings Using LLM-Generated Summaries For Scientific Abstracts—0
SemEval-2012 Task 6: A Pilot on Semantic Textual Similarity—0
SemEval-2013 Task 5: Evaluating Phrasal Semantics—0
SemEval-2014 Task 10: Multilingual Semantic Textual Similarity—0
SemEval-2014 Task 1: Evaluation of Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual Entailment—0
SemEval-2014 Task 3: Cross-Level Semantic Similarity—0
SemEval-2015 Task 1: Paraphrase and Semantic Similarity in Twitter (PIT)—0
SemEval-2015 Task 2: Semantic Textual Similarity, English, Spanish and Pilot on Interpretability—0
SemEval-2016 Task 14: Semantic Taxonomy Enrichment—0
SemEval-2016 Task 1: Semantic Textual Similarity, Monolingual and Cross-Lingual Evaluation—0
SemEval-2016 Task 2: Interpretable Semantic Textual Similarity—0
SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation—0
SemEval-2017 Task 2: Multilingual and Cross-lingual Semantic Word Similarity—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