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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 1751–1800 of 2381 papers

TitleStatusHype
Neural Keyphrase Generation: Analysis and Evaluation—0
Neural Keyphrase Generation: Analysis and Evaluation—0
Neural Models for Detecting Binary Semantic Textual Similarity for Algerian and MSA—0
Neural Network Architecture for Credibility Assessment of Textual Claims—0
Neural Networks for Semantic Textual Similarity—0
Neural Passage Retrieval with Improved Negative Contrast—0
Neural sentence generation from formal semantics—0
Neuro-Symbolic Artificial Intelligence (AI) for Intent based Semantic Communication—0
NeuSemSlice: Towards Effective DNN Model Maintenance via Neuron-level Semantic Slicing—0
News Aggregation with Diverse Viewpoint Identification Using Neural Embeddings and Semantic Understanding Models—0
NEWSFARM: the Largest Chinese Corpus for Long News Summarization—0
NgramQuery - Smart Information Extraction from Google N-gram using External Resources—0
NLP and Education: using semantic similarity to evaluate filled gaps in a large-scale Cloze test in the classroom—0
NLU-STR at SemEval-2024 Task 1: Generative-based Augmentation and Encoder-based Scoring for Semantic Textual Relatedness—0
Noise Contrastive Estimation-based Matching Framework for Low-resource Security Attack Pattern Recognition—0
Normalising Non-standardised Orthography in Algerian Code-switched User-generated Data—0
NORMAS at SemEval-2016 Task 1: SEMSIM: A Multi-Feature Approach to Semantic Text Similarity—0
Not just a matter of semantics: the relationship between visual similarity and semantic similarity—0
NSURL-2019 Shared Task 8: Semantic Question Similarity in Arabic—0
NTNU-CORE: Combining strong features for semantic similarity—0
NTNU: Measuring Semantic Similarity with Sublexical Feature Representations and Soft Cardinality—0
NTU NLP Lab System at SemEval-2018 Task 10: Verifying Semantic Differences by Integrating Distributional Information and Expert Knowledge—0
NTUSocialRec: An Evaluation Dataset Constructed from Microblogs for Recommendation Applications in Social Networks—0
NUIG-UNLP at SemEval-2016 Task 1: Soft Alignment and Deep Learning for Semantic Textual Similarity—0
NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models—0
Object-aware Long-short-range Spatial Alignment for Few-Shot Fine-Grained Image Classification—0
Off-topic Response Detection for Spontaneous Spoken English Assessment—0
Omni TM-AE: A Scalable and Interpretable Embedding Model Using the Full Tsetlin Machine State Space—0
On Adversarial Examples for Biomedical NLP Tasks—0
On Defining Smart Cities using Transformer Neural Networks—0
Online Asymmetric Similarity Learning for Cross-Modal Retrieval—0
Online Plagiarized Detection Through Exploiting Lexical, Syntax, and Semantic Information—0
On metric embedding for boosting semantic similarity computations—0
On Paraphrase Identification Corpora—0
On the Effectiveness of using Sentence Compression Models for Query-Focused Multi-Document Summarization—0
On the reliability and inter-annotator agreement of human semantic MT evaluation via HMEANT—0
Ontological Relations from Word Embeddings—0
Ontology Label Translation—0
Open Domain Question Answering Using Web Tables—0
OPI-JSA at SemEval-2017 Task 1: Application of Ensemble learning for computing semantic textual similarity—0
OPI: Semeval-2014 Task 3 System Description—0
OpticE: A Coherence Theory-Based Model for Link Prediction—0
Optimizing Retrieval-Augmented Generation with Elasticsearch for Enhanced Question-Answering Systems—0
Orthogonality of Syntax and Semantics within Distributional Spaces—0
Overfitting at SemEval-2016 Task 3: Detecting Semantically Similar Questions in Community Question Answering Forums with Word Embeddings—0
PaECTER: Patent-level Representation Learning using Citation-informed Transformers—0
Paint by Word—0
Pair Distance Distribution: A Model of Semantic Representation—0
Pairwise Word Interaction Modeling with Deep Neural Networks for Semantic Similarity Measurement—0
Pangloss: Fast Entity Linking in Noisy Text Environments—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