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

Showing 19011950 of 2381 papers

TitleStatusHype
Reinforcement Learning with Large Action Spaces for Neural Machine Translation0
Reinforcement Learning with Large Action Spaces for Neural Machine Translation0
Relating semantic similarity and semantic association to how humans label other people0
Relation Extraction Model Based on Semantic Enhancement Mechanism0
Relation extraction pattern ranking using word similarity0
Relevance-based Word Embedding0
RELiC: Retrieving Evidence for Literary Claims0
Representing Sentences as Low-Rank Subspaces0
Representing Verbs with Visual Argument Vectors0
Reserating the awesometastic: An automatic extension of the WordNet taxonomy for novel terms0
Resolving the Representational Problems of Polarity and Interaction between Process and State Verbs0
Graph-Based Recommendation System Enhanced with Community Detection0
Resources to Examine the Quality of Word Embedding Models Trained on n-Gram Data0
ResSim at SemEval-2017 Task 1: Multilingual Word Representations for Semantic Textual Similarity0
Rethinking Crowd Sourcing for Semantic Similarity0
Rethinking STS and NLI in Large Language Models0
Rethinking the constraints of multimodal fusion: case study in Weakly-Supervised Audio-Visual Video Parsing0
Retrieval-Enhanced Few-Shot Prompting for Speech Event Extraction0
Retrofitting Concept Vector Representations of Medical Concepts to Improve Estimates of Semantic Similarity and Relatedness0
Retrofitting Word Representations for Unsupervised Sense Aware Word Similarities0
Retrofitting Word Vectors of MeSH Terms to Improve Semantic Similarity Measures0
Multi-view Semantic Matching of Question retrieval using Fine-grained Semantic Representations0
Rev at SemEval-2016 Task 2: Aligning Chunks by Lexical, Part of Speech and Semantic Equivalence0
Reverse Probing: Evaluating Knowledge Transfer via Finetuned Task Embeddings for Coreference Resolution0
Rewarding Semantic Similarity under Optimized Alignments for AMR-to-Text Generation0
Rewriting Meaningful Sentences via Conditional BERT Sampling and an application on fooling text classifiers0
RICOH at SemEval-2016 Task 1: IR-based Semantic Textual Similarity Estimation0
ROB: Using Semantic Meaning to Recognize Paraphrases0
RoMe: A Robust Metric for Evaluating Natural Language Generation0
HTS-Attack: Heuristic Token Search for Jailbreaking Text-to-Image Models0
RTM at SemEval-2016 Task 1: Predicting Semantic Similarity with Referential Translation Machines and Related Statistics0
RTM at SemEval-2016 Task 1: Predicting Semantic Similarity with Referential Translation Machines and Related Statistics0
RTM at SemEval-2017 Task 1: Referential Translation Machines for Predicting Semantic Similarity0
RTM-DCU: Predicting Semantic Similarity with Referential Translation Machines0
RTM-DCU: Referential Translation Machines for Semantic Similarity0
RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation0
RUFINO at SemEval-2017 Task 2: Cross-lingual lexical similarity by extending PMI and word embeddings systems with a Swadesh's-like list0
Rule-based vs. Neural Net Approaches to Semantic Textual Similarity0
RUSSE: The First Workshop on Russian Semantic Similarity0
Saarland: Vector-based models of semantic textual similarity0
SAARSHEFF at SemEval-2016 Task 1: Semantic Textual Similarity with Machine Translation Evaluation Metrics and (eXtreme) Boosted Tree Ensembles0
SAGAN: An approach to Semantic Textual Similarity based on Textual Entailment0
SAIL-GRS: Grammar Induction for Spoken Dialogue Systems using CF-IRF Rule Similarity0
SAIL: Sentiment Analysis using Semantic Similarity and Contrast Features0
Saliency Attention and Semantic Similarity-Driven Adversarial Perturbation0
Saliency-Aware Regularized Graph Neural Network0
Same Referent, Different Words: Unsupervised Mining of Opaque Coreferent Mentions0
SAM-GAN: Self-Attention supporting Multi-stage Generative Adversarial Networks for text-to-image synthesis0
Samsung: Align-and-Differentiate Approach to Semantic Textual Similarity0
Samsung Poland NLP Team at SemEval-2016 Task 1: Necessity for diversity; combining recursive autoencoders, WordNet and ensemble methods to measure semantic similarity.0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SMARTRoBERTaDev Pearson Correlation92.8Unverified
2DeBERTa (large)Accuracy92.5Unverified
3SMART-BERTDev Pearson Correlation90Unverified
4MT-DNN-SMARTPearson Correlation0.93Unverified
5StructBERTRoBERTa ensemblePearson Correlation0.93Unverified
6Mnet-SimPearson Correlation0.93Unverified
7XLNet (single model)Pearson Correlation0.93Unverified
8ALBERTPearson Correlation0.93Unverified
9T5-11BPearson Correlation0.93Unverified
10RoBERTaPearson Correlation0.92Unverified
#ModelMetricClaimedVerifiedStatus
1AnglE-UAESpearman Correlation84.54Unverified
2ST5-XXLSpearman Correlation82.63Unverified
3ST5-LargeSpearman Correlation81.83Unverified
4ST5-XLSpearman Correlation81.66Unverified
5ST5-BaseSpearman Correlation81.14Unverified
6MPNet-multilingualSpearman Correlation80.73Unverified
7SGPT-5.8B-nliSpearman Correlation80.53Unverified
8MPNetSpearman Correlation80.28Unverified
9MiniLM-L12Spearman Correlation79.8Unverified
10SimCSE-BERT-supSpearman Correlation79.12Unverified
#ModelMetricClaimedVerifiedStatus
1MT-DNN-SMARTAccuracy93.7Unverified
2ALBERTAccuracy93.4Unverified
3RoBERTa (ensemble)Accuracy92.3Unverified
4BigBirdF191.5Unverified
5StructBERTRoBERTa ensembleAccuracy91.5Unverified
6FLOATER-largeAccuracy91.4Unverified
7SMARTAccuracy91.3Unverified
8RoBERTa-large 355M (MLP quantized vector-wise, fine-tuned)Accuracy91Unverified
9RoBERTa-large 355M + Entailment as Few-shot LearnerF191Unverified
10SpanBERTAccuracy90.9Unverified
#ModelMetricClaimedVerifiedStatus
1PromCSE-RoBERTa-large (0.355B)Spearman Correlation0.82Unverified
2PromptEOL+CSE+LLaMA-30BSpearman Correlation0.82Unverified
3PromptEOL+CSE+OPT-13BSpearman Correlation0.82Unverified
4SimCSE-RoBERTalargeSpearman Correlation0.82Unverified
5PromptEOL+CSE+OPT-2.7BSpearman Correlation0.81Unverified
6SentenceBERTSpearman Correlation0.75Unverified
7SRoBERTa-NLI-baseSpearman Correlation0.74Unverified
8SRoBERTa-NLI-largeSpearman Correlation0.74Unverified
9Dino (STS/̄🦕)Spearman Correlation0.74Unverified
10SBERT-NLI-largeSpearman Correlation0.74Unverified
#ModelMetricClaimedVerifiedStatus
1AnglE-LLaMA-7BSpearman Correlation0.91Unverified
2AnglE-LLaMA-7B-v2Spearman Correlation0.91Unverified
3PromptEOL+CSE+LLaMA-30BSpearman Correlation0.9Unverified
4PromptEOL+CSE+OPT-13BSpearman Correlation0.9Unverified
5PromptEOL+CSE+OPT-2.7BSpearman Correlation0.9Unverified
6PromCSE-RoBERTa-large (0.355B)Spearman Correlation0.89Unverified
7Trans-Encoder-BERT-large-bi (unsup.)Spearman Correlation0.89Unverified
8Trans-Encoder-BERT-large-cross (unsup.)Spearman Correlation0.88Unverified
9Trans-Encoder-RoBERTa-large-cross (unsup.)Spearman Correlation0.88Unverified
10SimCSE-RoBERTa-largeSpearman Correlation0.87Unverified