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 20512100 of 2381 papers

TitleStatusHype
Retro-li: Small-Scale Retrieval Augmented Generation Supporting Noisy Similarity Searches and Domain Shift GeneralizationCode0
A Brief Study on the Effects of Training Generative Dialogue Models with a Semantic lossCode0
Doubly-Trained Adversarial Data Augmentation for Neural Machine TranslationCode0
ReVal: A Simple and Effective Machine Translation Evaluation Metric Based on Recurrent Neural NetworksCode0
Table2Vec: Neural Word and Entity Embeddings for Table Population and RetrievalCode0
Improving Semantic Relevance for Sequence-to-Sequence Learning of Chinese Social Media Text SummarizationCode0
Audio Caption in a Car Setting with a Sentence-Level LossCode0
Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word VectorsCode0
Improving Sentence Embeddings with Automatic Generation of Training Data Using Few-shot ExamplesCode0
Multimodal Visual Concept Learning with Weakly Supervised TechniquesCode0
Revisiting Cosine Similarity via Normalized ICA-transformed EmbeddingsCode0
Revisiting Semantic Representation and Tree Search for Similar Question RetrievalCode0
Zero-Shot Object Goal Visual Navigation With Class-Independent Relationship NetworkCode0
A Bilingual Generative Transformer for Semantic Sentence EmbeddingCode0
Harnessing Frozen Unimodal Encoders for Flexible Multimodal AlignmentCode0
Uncovering the Semantics of Wikipedia CategoriesCode0
Cognition-aware Cognate DetectionCode0
A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence RepresentationsCode0
Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity MatchingCode0
HACD: Harnessing Attribute Semantics and Mesoscopic Structure for Community DetectionCode0
Robust Privacy Amidst Innovation with Large Language Models Through a Critical Assessment of the RisksCode0
RobustSentEmbed: Robust Sentence Embeddings Using Adversarial Self-Supervised Contrastive LearningCode0
Increasing In-Class Similarity by Retrofitting Embeddings with Demographic InformationCode0
AEON: A Method for Automatic Evaluation of NLP Test CasesCode0
Indra: A Word Embedding and Semantic Relatedness ServerCode0
COD3S: Diverse Generation with Discrete Semantic SignaturesCode0
CLIMB-3D: Continual Learning for Imbalanced 3D Instance SegmentationCode0
Guiding and Diversifying LLM-Based Story Generation via Answer Set ProgrammingCode0
Specializing Unsupervised Pretraining Models for Word-Level Semantic SimilarityCode0
What If: Generating Code to Answer Simulation QuestionsCode0
INO at Factify 2: Structure Coherence based Multi-Modal Fact VerificationCode0
NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching SummarizationCode0
UoW: NLP techniques developed at the University of Wolverhampton for Semantic Similarity and Textual EntailmentCode0
Near-lossless Binarization of Word EmbeddingsCode0
Training Complex Models with Multi-Task Weak SupervisionCode0
Sew-Embed at SemEval-2017 Task 2: Language-Independent Concept Representations from a Semantically Enriched WikipediaCode0
NER4ID at SemEval-2022 Task 2: Named Entity Recognition for Idiomaticity DetectionCode0
GSTran: Joint Geometric and Semantic Coherence for Point Cloud SegmentationCode0
Instance Smoothed Contrastive Learning for Unsupervised Sentence EmbeddingCode0
GMFL-Net: A Global Multi-geometric Feature Learning Network for Repetitive Action CountingCode0
GMA3D: Local-Global Attention Learning to Estimate Occluded Motions of Scene FlowCode0
Automatic Design of Semantic Similarity Ensembles Using Grammatical EvolutionCode0
Integrating Visual and Semantic Similarity Using Hierarchies for Image RetrievalCode0
Neural Activation Semantic Models: Computational lexical semantic models of localized neural activationsCode0
Don’t Judge a Language Model by Its Last Layer: Contrastive Learning with Layer-Wise Attention PoolingCode0
Don't Judge a Language Model by Its Last Layer: Contrastive Learning with Layer-Wise Attention PoolingCode0
Training Cross-Lingual embeddings for Setswana and SepediCode0
Autoencoding Pixies: Amortised Variational Inference with Graph Convolutions for Functional Distributional SemanticsCode0
Interpretable Word Sense Representations via Definition Generation: The Case of Semantic Change AnalysisCode0
Interpretation of Semantic Tweet RepresentationsCode0
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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