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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 2001–2050 of 2381 papers

TitleStatusHype
Cleaning noisy wordnets—0
CLEAR: Contrastive Learning for Sentence Representation—0
CLISTER : A Corpus for Semantic Textual Similarity in French Clinical Narratives—0
CLISTER : Un corpus pour la similarité sémantique textuelle dans des cas cliniques en français (CLISTER : A Corpus for Semantic Textual Similarity in French Clinical Narratives)—0
Cluster Analysis with Deep Embeddings and Contrastive Learning—0
Clustering and Diversifying Web Search Results with Graph-Based Word Sense Induction—0
Clustering Prominent People and Organizations in Topic-Specific Text Corpora—0
ClusterLog: Clustering Logs for Effective Log-based Anomaly Detection—0
CNGL-CORE: Referential Translation Machines for Measuring Semantic Similarity—0
CNGL: Grading Student Answers by Acts of Translation—0
CNRC at SemEval-2016 Task 1: Experiments in Crosslingual Semantic Textual Similarity—0
Co-Attention Hierarchical Network: Generating Coherent Long Distractors for Reading Comprehension—0
Code Clone Detection based on Event Embedding and Event Dependency—0
CogALex-V Shared Task: GHHH - Detecting Semantic Relations via Word Embeddings—0
Cognate Identification using Machine Translation—0
Cognitively Motivated Distributional Representations of Meaning—0
Column sampling based discrete supervised hashing—0
Combinaison d'information visuelle, conceptuelle, et contextuelle pour la construction automatique de hierarchies semantiques adaptees a l'annotation d'images—0
Combining Contrastive Learning and Knowledge Graph Embeddings to develop medical word embeddings for the Italian language—0
Combining Structured and Unstructured Knowledge in an Interactive Search Dialogue System—0
Combining Word Embeddings and N-grams for Unsupervised Document Summarization—0
Common Variable Learning and Invariant Representation Learning using Siamese Neural Networks—0
Community Search in Time-dependent Road-social Attributed Networks—0
Comparing Apples to Apple: The Effects of Stemmers on Topic Models—0
Comparing Approaches for Automatic Question Identification—0
Comparing Data Sources and Architectures for Deep Visual Representation Learning in Semantics—0
Comparing scalable strategies for generating numerical perspectives—0
Comparison and Combination of Sentence Embeddings Derived from Different Supervision Signals—0
Comparison of Paragram and GloVe Results for Similarity Benchmarks—0
Complex Verbs are Different: Exploring the Visual Modality in Multi-Modal Models to Predict Compositionality—0
Compositional Distributional Semantics Models in Chunk-based Smoothed Tree Kernels—0
Compressing Sentence Representation via Homomorphic Projective Distillation—0
Computing Semantic Text Similarity Using Rich Features—0
Concrete Models and Empirical Evaluations for the Categorical Compositional Distributional Model of Meaning—0
Conditional Attention Networks for Distilling Knowledge Graphs in Recommendation—0
ConIsI: A Contrastive Framework with Inter-sentence Interaction for Self-supervised Sentence Representation—0
Conjuring Semantic Similarity—0
Connecting the Dots: Inferring Patent Phrase Similarity with Retrieved Phrase Graphs—0
Connecting the Dots: Leveraging Spatio-Temporal Graph Neural Networks for Accurate Bangla Sign Language Recognition—0
Conservative Bias in Large Language Models: Measuring Relation Predictions—0
Consistency of Responses and Continuations Generated by Large Language Models on Social Media—0
Constructing a Norm for Children's Scientific Drawing: Distribution Features Based on Semantic Similarity of Large Language Models—0
Content Selection through Paraphrase Detection: Capturing different Semantic Realisations of the Same Idea—0
Context-Aware Human Behavior Prediction Using Multimodal Large Language Models: Challenges and Insights—0
Context-Dependent Translation Selection Using Convolutional Neural Network—0
Context Effects on Human Judgments of Similarity—0
Contextual ASR Error Handling with LLMs Augmentation for Goal-Oriented Conversational AI—0
Contextualized Usage-Based Material Selection—0
Contextualizing the Limits of Model & Evaluation Dataset Curation on Semantic Similarity Classification Tasks—0
Context Vector Disambiguation for Bilingual Lexicon Extraction from Comparable Corpora—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