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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 2101–2125 of 2381 papers

TitleStatusHype
RTM-DCU: Referential Translation Machines for Semantic Similarity—0
ECNU: One Stone Two Birds: Ensemble of Heterogenous Measures for Semantic Relatedness and Textual Entailment—0
ECNU: Leveraging on Ensemble of Heterogeneous Features and Information Enrichment for Cross Level Semantic Similarity Estimation—0
SimCompass: Using Deep Learning Word Embeddings to Assess Cross-level Similarity—0
Duluth : Measuring Cross-Level Semantic Similarity with First and Second Order Dictionary Overlaps—0
Compositional Distributional Semantics Models in Chunk-based Smoothed Tree Kernels—0
SSMT:A Machine Translation Evaluation View To Paragraph-to-Sentence Semantic Similarity—0
Illinois-LH: A Denotational and Distributional Approach to Semantics—0
Contrasting Syntagmatic and Paradigmatic Relations: Insights from Distributional Semantic Models—0
FBK-TR: Applying SVM with Multiple Linguistic Features for Cross-Level Semantic Similarity—0
OPI: Semeval-2014 Task 3 System Description—0
Identifying semantic relations in a specialized corpus through distributional analysis of a cooccurrence tensor—0
DLS@CU: Sentence Similarity from Word Alignment—0
UoW: NLP techniques developed at the University of Wolverhampton for Semantic Similarity and Textual EntailmentCode0
NTNU: Measuring Semantic Similarity with Sublexical Feature Representations and Soft Cardinality—0
TCDSCSS: Dimensionality Reduction to Evaluate Texts of Varying Lengths - an IR Approach—0
UoW: Multi-task Learning Gaussian Process for Semantic Textual Similarity—0
TeamZ: Measuring Semantic Textual Similarity for Spanish Using an Overlap-Based Approach—0
HulTech: A General Purpose System for Cross-Level Semantic Similarity based on Anchor Web Counts—0
The Meaning Factory: Formal Semantics for Recognizing Textual Entailment and Determining Semantic Similarity—0
DIT: Summarisation and Semantic Expansion in Evaluating Semantic Similarity—0
UNIBA: Combining Distributional Semantic Models and Word Sense Disambiguation for Textual Similarity—0
An analysis of textual inference in German customer emails—0
Meerkat Mafia: Multilingual and Cross-Level Semantic Textual Similarity Systems—0
tucSage: Grammar Rule Induction for Spoken Dialogue Systems via Probabilistic Candidate Selection—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