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 14511500 of 2381 papers

TitleStatusHype
Improving Knowledge-Aware Dialogue Response Generation by Using Human-Written Prototype Dialogues0
Improving Legal Entity Recognition Using a Hybrid Transformer Model and Semantic Filtering Approach0
Improving Long Document Topic Segmentation Models With Enhanced Coherence Modeling0
Adaptive Logit Adjustment Loss for Long-Tailed Visual Recognition0
Improving Multilingual Semantic Textual Similarity with Shared Sentence Encoder for Low-resource Languages0
Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding0
Improving Parallel Data Identification using Iteratively Refined Sentence Alignments and Bilingual Mappings of Pre-trained Language Models0
Improving RAG Retrieval via Propositional Content Extraction: a Speech Act Theory Approach0
Improving Relevance Quality in Product Search using High-Precision Query-Product Semantic Similarity0
Improving ROUGE for Timeline Summarization0
Improving Semantic Similarity Calculation of Japanese Text for MT Evaluation0
Improving Semantic Similarity Measure Within a Recommender System Based-on RDF Graphs0
Improving sparse word similarity models with asymmetric measures0
Improving Text Normalization via Unsupervised Model and Discriminative Reranking0
Improving Text Semantic Similarity Modeling through a 3D Siamese Network0
Improving Trace Link Recommendation by Using Non-Isotropic Distances and Combinations0
Improving Verb Metaphor Detection by Propagating Abstractness to Words, Phrases and Individual Senses0
INAOE\_UPV-CORE: Extracting Word Associations from Document Corpora to estimate Semantic Textual Similarity0
In-Context Experience Replay Facilitates Safety Red-Teaming of Text-to-Image Diffusion Models0
In-Context Learning for Few-Shot Nested Named Entity Recognition0
Incorporating Contextual and Syntactic Structures Improves Semantic Similarity Modeling0
Incorporating Semantic Textual Similarity and Lexical Matching for Information Retrieval0
Incorporating Word Embeddings into Open Directory Project based Large-scale Classification0
IndoWordNet::Similarity- Computing Semantic Similarity and Relatedness using IndoWordNet0
Information Navigation System with Discovering User Interests0
Informativeness Constraints and Compositionality0
Injecting Lexical Contrast into Word Vectors by Guiding Vector Space Specialisation0
Inpatient2Vec: Medical Representation Learning for Inpatients0
InsertRank: LLMs can reason over BM25 scores to Improve Listwise Reranking0
InsightNet: Structured Insight Mining from Customer Feedback0
Inspire at SemEval-2016 Task 2: Interpretable Semantic Textual Similarity Alignment based on Answer Set Programming0
Instance-aware Image and Sentence Matching with Selective Multimodal LSTM0
Instance Cross Entropy for Deep Metric Learning0
Integrating Distributional and Lexical Information for Semantic Classification of Words using MRMF0
Interactive Variance Attention based Online Spoiler Detection for Time-Sync Comments0
Internal Wasserstein Distance for Adversarial Attack and Defense0
Interpretable Company Similarity with Sparse Autoencoders0
Interpretable Semantic Textual Similarity: Finding and explaining differences between sentences0
Interpretable Semantic Vectors from a Joint Model of Brain- and Text- Based Meaning0
Inter-Weighted Alignment Network for Sentence Pair Modeling0
Intra-Topic Variability Normalization based on Linear Projection for Topic Classification0
Intrinsic Evaluations of Word Embeddings: What Can We Do Better?0
Intrinsic vs. Extrinsic Evaluation of Czech Sentence Embeddings: Semantic Relevance Doesn't Help with MT Evaluation0
Introducing two Vietnamese Datasets for Evaluating Semantic Models of (Dis-)Similarity and Relatedness0
Investigating Antigram Behaviour using Distributional Semantics0
Investigating Continual Pretraining in Large Language Models: Insights and Implications0
Investigating Entropy for Extractive Document Summarization0
Investigating Large Language Models for Financial Causality Detection in Multilingual Setup0
Investigating the Role of Negatives in Contrastive Representation Learning0
IRCMS at SemEval-2018 Task 7 : Evaluating a basic CNN Method and Traditional Pipeline Method for Relation Classification0
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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