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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 1401–1450 of 2381 papers

TitleStatusHype
Word similarity using constructions as contextual features—0
Word to Sentence Visual Semantic Similarity for Caption Generation: Lessons Learned—0
Word Vector Space Specialisation—0
Word vectors, reuse, and replicability: Towards a community repository of large-text resources—0
yiGou: A Semantic Text Similarity Computing System Based on SVM—0
YiSi - a Unified Semantic MT Quality Evaluation and Estimation Metric for Languages with Different Levels of Available Resources—0
Zero-shot Event Causality Identification with Question Answering—0
Zero-Shot Learning via Semantic Similarity Embedding—0
Zero-shot Learning with Minimum Instruction to Extract Social Determinants and Family History from Clinical Notes using GPT Model—0
Zero-Shot Text Matching for Automated Auditing using Sentence Transformers—0
H_eval: A new hybrid evaluation metric for automatic speech recognition tasks—0
HyCIR: Boosting Zero-Shot Composed Image Retrieval with Synthetic Labels—0
HyperFree: A Channel-adaptive and Tuning-free Foundation Model for Hyperspectral Remote Sensing Imagery—0
Hypernym Mercury: Token Optimization Through Semantic Field Constriction And Reconstruction From Hypernyms. A New Text Compression Method—0
Hypernyms Through Intra-Article Organization in Wikipedia—0
Hyperspherical Query Likelihood Models with Word Embeddings—0
IBM\_EG-CORE: Comparing multiple Lexical and NE matching features in measuring Semantic Textual similarity—0
ICRC-HIT: A Deep Learning based Comment Sequence Labeling System for Answer Selection Challenge—0
Identical and Fraternal Twins: Fine-Grained Semantic Contrastive Learning of Sentence Representations—0
Identifying Bad Semantic Neighbors for Improving Distributional Thesauri—0
Identifying Essential Hub Genes and Protein Complexes in Malaria GO Data using Semantic Similarity Measures—0
Identifying hypernyms in distributional semantic spaces—0
Identifying Prominent Arguments in Online Debates Using Semantic Textual Similarity—0
Identifying semantic relations in a specialized corpus through distributional analysis of a cooccurrence tensor—0
IEKM: A Model Incorporating External Keyword Matrices—0
If Sentences Could See: Investigating Visual Information for Semantic Textual Similarity—0
Igevorse at SemEval-2018 Task 10: Exploring an Impact of Word Embeddings Concatenation for Capturing Discriminative Attributes—0
IHS-RD-Belarus at SemEval-2016 Task 1: Multistage Approach for Measuring Semantic Similarity—0
IIRG: A Naive Approach to Evaluating Phrasal Semantics—0
IISCNLP at SemEval-2016 Task 2: Interpretable STS with ILP based Multiple Chunk Aligner—0
IIT (BHU): System Description for LSDSem'17 Shared Task—0
iKernels-Core: Tree Kernel Learning for Textual Similarity—0
Illinois-LH: A Denotational and Distributional Approach to Semantics—0
Image Colorization Using a Deep Convolutional Neural Network—0
Image Generative Semantic Communication with Multi-Modal Similarity Estimation for Resource-Limited Networks—0
Image Pivoting for Learning Multilingual Multimodal Representations—0
Implicit Entity Recognition in Clinical Documents—0
Improved Semantic Representation for Domain-Specific Entities—0
Improving astroBERT using Semantic Textual Similarity—0
Improving Brain-to-Image Reconstruction via Fine-Grained Text Bridging—0
Improving Context-Aware Semantic Relationships in Sparse Mobile Datasets—0
Improving Contextual Representation with Gloss Regularized Pre-training—0
Improving Contextual Representation with Gloss Regularized Pre-training—0
Improving Correlation with Human Judgments by Integrating Semantic Similarity with Second--Order Vectors—0
Improving Cross-lingual Representation for Semantic Retrieval with Code-switching—0
Improving Cross-Lingual Transfer for Event Argument Extraction with Language-Universal Sentence Structures—0
Improving embedding with contrastive fine-tuning on small datasets with expert-augmented scores—0
Improving Generalization of Image Captioning with Unsupervised Prompt Learning—0
Improving General Text Embedding Model: Tackling Task Conflict and Data Imbalance through Model Merging—0
Improving ICD-based semantic similarity by accounting for varying degrees of comorbidity—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