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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 1451–1500 of 2381 papers

TitleStatusHype
Graded Suspiciousness of Adversarial Texts to Human—0
SXUCFN-Core: STS Models Integrating FrameNet Parsing Information—0
Synergistic Union of Word2Vec and Lexicon for Domain Specific Semantic Similarity—0
System Description for the CommonGen task with the POINTER model—0
T3: A Novel Zero-shot Transfer Learning Framework Iteratively Training on an Assistant Task for a Target Task—0
TabSim: A Siamese Neural Network for Accurate Estimation of Table Similarity—0
TAKELAB: Medical Information Extraction and Linking with MINERAL—0
TakeLab-QA at SemEval-2017 Task 3: Classification Experiments for Answer Retrieval in Community QA—0
TakeLab: Systems for Measuring Semantic Text Similarity—0
Talla at SemEval-2017 Task 3: Identifying Similar Questions Through Paraphrase Detection—0
Target-Centric Features for Translation Quality Estimation—0
Targeted and Troublesome: Tracking and Advertising on Children's Websites—0
Target word prediction and paraphasia classification in spoken discourse—0
TA-SBERT: Token Attention Sentence-BERT for Improving Sentence Representation—0
Task Formulation Matters When Learning Continuously: A Case Study in Visual Question Answering—0
Task-Oriented Intrinsic Evaluation of Semantic Textual Similarity—0
TATO: Leveraging on Multiple Strategies for Semantic Textual Similarity—0
TCDSCSS: Dimensionality Reduction to Evaluate Texts of Varying Lengths - an IR Approach—0
Team dina at SemEval-2022 Task 8: Pre-trained Language Models as Baselines for Semantic Similarity—0
Team GPLSI. Approach for automated fact checking—0
TeamZ: Measuring Semantic Textual Similarity for Spanish Using an Overlap-Based Approach—0
Temporal Analysis of Entity Relatedness and its Evolution using Wikipedia and DBpedia—0
Term Based Semantic Clusters for Very Short Text Classification—0
Testing the limits of unsupervised learning for semantic similarity—0
TexIm FAST: Text-to-Image Representation for Semantic Similarity Evaluation using Transformers—0
Text Classification with Negative Supervision—0
Text Emotion Distribution Learning from Small Sample: A Meta-Learning Approach—0
Text Encoders Lack Knowledge: Leveraging Generative LLMs for Domain-Specific Semantic Textual Similarity—0
TextGraphs-16 Natural Language Premise Selection Task: Zero-Shot Premise Selection with Prompting Generative Language Models—0
Text-Guided Unsupervised Latent Transformation for Multi-Attribute Image Manipulation—0
Texts in, meaning out: neural language models in semantic similarity task for Russian—0
The BQ Corpus: A Large-scale Domain-specific Chinese Corpus For Sentence Semantic Equivalence Identification—0
The Case for Perspective in Multimodal Datasets—0
The Challenge of Composition in Distributional and Formal Semantics—0
The Corpus Replication Task—0
The CW Corpus: A New Resource for Evaluating the Identification of Complex Words—0
Dynamics and Reachability of Learning Tasks—0
The Effect of Negative Sampling Strategy on Capturing Semantic Similarity in Document Embeddings—0
The Effects of Semantic Annotations on Precision Parse Ranking—0
The Meaning Factory: Formal Semantics for Recognizing Textual Entailment and Determining Semantic Similarity—0
The MeSH-gram Neural Network Model: Extending Word Embedding Vectors with MeSH Concepts for UMLS Semantic Similarity and Relatedness in the Biomedical Domain—0
The Russian-focused embedders' exploration: ruMTEB benchmark and Russian embedding model design—0
The semantic similarity ensemble—0
The Sound of Healthcare: Improving Medical Transcription ASR Accuracy with Large Language Models—0
The strange geometry of skip-gram with negative sampling—0
The Struggle with Academic Plagiarism: Approaches based on Semantic Similarity—0
The TALP-UPC Approach to System Selection: Asiya Features and Pairwise Classification Using Random Forests—0
The Wisdom of MaSSeS: Majority, Subjectivity, and Semantic Similarity in the Evaluation of VQA—0
The Word Analogy Testing Caveat—0
The word entropy of natural languages—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