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

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Showing 1151–1175 of 2381 papers

TitleStatusHype
Ebiquity: Paraphrase and Semantic Similarity in Twitter using Skipgrams—0
EASE: Entity-Aware Contrastive Learning of Sentence Embedding—0
A partition-based similarity for classification distributions—0
Dynamic Few-Shot Learning for Knowledge Graph Question Answering—0
Bootstrapping Unsupervised Bilingual Lexicon Induction—0
Duplicate Detection in a Knowledge Base with PIKA—0
A Paraphrase and Semantic Similarity Detection System for User Generated Short-Text Content on Microblogs—0
A Graph-theoretic Summary Evaluation for ROUGE—0
Duluth : Measuring Cross-Level Semantic Similarity with First and Second Order Dictionary Overlaps—0
Dual Tensor Model for Detecting Asymmetric Lexico-Semantic Relations—0
Boosting the Performance of Transformer Architectures for Semantic Textual Similarity—0
Dual-Stream Knowledge-Preserving Hashing for Unsupervised Video Retrieval—0
RealignDiff: Boosting Text-to-Image Diffusion Model with Coarse-to-fine Semantic Re-alignment—0
A Parametric Similarity Method: Comparative Experiments based on Semantically Annotated Large Datasets—0
DT\_Team at SemEval-2017 Task 1: Semantic Similarity Using Alignments, Sentence-Level Embeddings and Gaussian Mixture Model Output—0
DTSim at SemEval-2016 Task 2: Interpreting Similarity of Texts Based on Automated Chunking, Chunk Alignment and Semantic Relation Prediction—0
DTSim at SemEval-2016 Task 1: Semantic Similarity Model Including Multi-Level Alignment and Vector-Based Compositional Semantics—0
DTAFA: Decoupled Training Architecture for Efficient FAQ Retrieval—0
Boosting Few-Shot Learning With Adaptive Margin Loss—0
A Graph Based Semi-Supervised Approach for Analysis of Derivational Nouns in Sanskrit—0
A comparison of models of word meaning in context—0
Do We Need to Differentiate Negative Candidates Before Training a Neural Ranker?—0
Book Reviews: Semantic Similarity from Natural Language and Ontology Analysis by S\'ebastien Harispe, Sylvie Ranwez, Stefan Janaqi, and Jacky Montmain—0
Do Smaller Language Models Answer Contextualised Questions Through Memorisation Or Generalisation?—0
BiVRec: Bidirectional View-based Multimodal Sequential Recommendation—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