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 951–1000 of 2381 papers

TitleStatusHype
Evaluation on Second Language Collocational Congruency with Computational Semantic Similarity—0
GiBERT: Introducing Linguistic Knowledge into BERT through a Lightweight Gated Injection Method—0
GiCCS: A German in-Context Conversational Similarity Benchmark—0
GKR: the Graphical Knowledge Representation for semantic parsing—0
Evaluation of taxonomic and neural embedding methods for calculating semantic similarity—0
Evaluation of Simple Distributional Compositional Operations on Longer Texts—0
CFILT-CORE: Semantic Textual Similarity using Universal Networking Language—0
Evaluation of Semantic Search and its Role in Retrieved-Augmented-Generation (RAG) for Arabic Language—0
Evaluation of BERT and ALBERT Sentence Embedding Performance on Downstream NLP Tasks—0
Evaluation Datasets for Cross-lingual Semantic Textual Similarity—0
Gold Standard Online Debates Summaries and First Experiments Towards Automatic Summarization of Online Debate Data—0
GPTSee: Enhancing Moment Retrieval and Highlight Detection via Description-Based Similarity Features—0
Graph-Augmented Cyclic Learning Framework for Similarity Estimation of Medical Clinical Notes—0
Evaluation by Association: A Systematic Study of Quantitative Word Association Evaluation—0
Center-wise Local Image Mixture For Contrastive Representation Learning—0
GrFormer: A Novel Transformer on Grassmann Manifold for Infrared and Visible Image Fusion—0
Grounding Action Descriptions in Videos—0
Grounding Semantics in Olfactory Perception—0
GSI-UPM at SemEval-2019 Task 5: Semantic Similarity and Word Embeddings for Multilingual Detection of Hate Speech Against Immigrants and Women on Twitter—0
Are We Truly Forgetting? A Critical Re-examination of Machine Unlearning Evaluation Protocols—0
A Large-Scale Multilingual Disambiguation of Glosses—0
A Contrastive Framework for Learning Sentence Representations from Pairwise and Triple-wise Perspective in Angular Space—0
Evaluating Topic Coherence Using Distributional Semantics—0
Evaluating the Utility of Model Configurations and Data Augmentation on Clinical Semantic Textual Similarity—0
Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examples—0
Evaluating the Stability of Embedding-based Word Similarities—0
Hardness of Samples Need to be Quantified for a Reliable Evaluation System: Exploring Potential Opportunities with a New Task—0
CDTDS: Predicting Paraphrases in Twitter via Support Vector Regression—0
Harnessing label semantics to extract higher performance under noisy label for Company to Industry matching—0
HashAttention: Semantic Sparsity for Faster Inference—0
Hashtags are (not) judgemental: The untold story of Lok Sabha elections 2019—0
HCCL at SemEval-2017 Task 2: Combining Multilingual Word Embeddings and Transliteration Model for Semantic Similarity—0
Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks—0
HD-RAG: Retrieval-Augmented Generation for Hybrid Documents Containing Text and Hierarchical Tables—0
Headerless, Quoteless, but not Hopeless? Using Pairwise Email Classification to Disentangle Email Threads—0
HENRY-CORE: Domain Adaptation and Stacking for Text Similarity—0
HHU at SemEval-2016 Task 1: Multiple Approaches to Measuring Semantic Textual Similarity—0
HHU at SemEval-2017 Task 2: Fast Hash-Based Embeddings for Semantic Word Similarity Assessment—0
Evaluating text coherence based on semantic similarity graph—0
CausalRAG: Integrating Causal Graphs into Retrieval-Augmented Generation—0
Evaluating Tag Recommendations for E-Book Annotation Using a Semantic Similarity Metric—0
Evaluating semantic models with word-sentence relatedness—0
Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan—0
Highlights of Semantics in Multi-objective Genetic Programming—0
A Large Resource of Patterns for Verbal Paraphrases—0
Evaluating Retrieval Augmented Generative Models for Document Queries in Transportation Safety—0
Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces—0
Evaluating Multimodal Representations on Sentence Similarity: vSTS, Visual Semantic Textual Similarity Dataset—0
HLTC-HKUST: A Neural Network Paraphrase Classifier using Translation Metrics, Semantic Roles and Lexical Similarity Features—0
Are Manually Prepared Affective Lexicons Really Useful for Sentiment Analysis—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
8T5-11BPearson Correlation0.93—Unverified
9ALBERTPearson 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