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 1101–1150 of 2381 papers

TitleStatusHype
Informativeness Constraints and Compositionality—0
Cross-modal Deep Metric Learning with Multi-task Regularization—0
Information Navigation System with Discovering User Interests—0
Cross-media Similarity Metric Learning with Unified Deep Networks—0
Automated Fact-Checking of Claims in Argumentative Parliamentary Debates—0
Advancing Large Language Models for Spatiotemporal and Semantic Association Mining of Similar Environmental Events—0
KnCe2013-CORE:Semantic Text Similarity by use of Knowledge Bases—0
Injecting Lexical Contrast into Word Vectors by Guiding Vector Space Specialisation—0
Automated Feedback Loops to Protect Text Simplification with Generative AI from Information Loss—0
Inpatient2Vec: Medical Representation Learning for Inpatients—0
Knowing the Author by the Company His Words Keep—0
InsightNet: Structured Insight Mining from Customer Feedback—0
Inspire at SemEval-2016 Task 2: Interpretable Semantic Textual Similarity Alignment based on Answer Set Programming—0
Instance-aware Image and Sentence Matching with Selective Multimodal LSTM—0
IndoWordNet::Similarity- Computing Semantic Similarity and Relatedness using IndoWordNet—0
Cross-Media Scientific Research Achievements Retrieval Based on Deep Language Model—0
Czech News Dataset for Semantic Textual Similarity—0
Automated Assessment of Students' Code Comprehension using LLMs—0
Incorporating Word Embeddings into Open Directory Project based Large-scale Classification—0
Incorporating Semantic Textual Similarity and Lexical Matching for Information Retrieval—0
DalGTM at SemEval-2016 Task 1: Importance-Aware Compositional Approach to Short Text Similarity—0
Interactive Variance Attention based Online Spoiler Detection for Time-Sync Comments—0
Cross-lingual Transfer or Machine Translation? On Data Augmentation for Monolingual Semantic Textual Similarity—0
Interpretable Company Similarity with Sparse Autoencoders—0
Interpretable Semantic Textual Similarity: Finding and explaining differences between sentences—0
Interpretable Semantic Vectors from a Joint Model of Brain- and Text- Based Meaning—0
Data Driven Content Creation using Statistical and Natural Language Processing Techniques for Financial Domain—0
Anaphora Resolution in Dialogue: Description of the DFKI-TalkingRobots System for the CODI-CRAC 2021 Shared-Task—0
Data-driven Paraphrasing and Stylistic Harmonization—0
Inter-Weighted Alignment Network for Sentence Pair Modeling—0
Analyzing the impact of climate change on critical infrastructure from the scientific literature: A weakly supervised NLP approach—0
Incorporating Contextual and Syntactic Structures Improves Semantic Similarity Modeling—0
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 Evaluation—0
Introducing two Vietnamese Datasets for Evaluating Semantic Models of (Dis-)Similarity and Relatedness—0
In-Context Learning for Few-Shot Nested Named Entity Recognition—0
Investigating Antigram Behaviour using Distributional Semantics—0
Investigating Continual Pretraining in Large Language Models: Insights and Implications—0
In-Context Experience Replay Facilitates Safety Red-Teaming of Text-to-Image Diffusion Models—0
Investigating Large Language Models for Financial Causality Detection in Multilingual Setup—0
Cross-Lingual Syntactically Informed Distributed Word Representations—0
Automatic Extraction of Turkish Hypernym-Hyponym Pairs From Large Corpus—0
KGV: Integrating Large Language Models with Knowledge Graphs for Cyber Threat Intelligence Credibility Assessment—0
IRCMS at SemEval-2018 Task 7 : Evaluating a basic CNN Method and Traditional Pipeline Method for Relation Classification—0
INAOE\_UPV-CORE: Extracting Word Associations from Document Corpora to estimate Semantic Textual Similarity—0
Cross Lingual Sentiment Analysis using Modified BRAE—0
ISCAS\_NLP at SemEval-2016 Task 1: Sentence Similarity Based on Support Vector Regression using Multiple Features—0
Is Contrasting All You Need? Contrastive Learning for the Detection and Attribution of AI-generated Text—0
Is Cosine-Similarity of Embeddings Really About Similarity?—0
Improving Verb Metaphor Detection by Propagating Abstractness to Words, Phrases and Individual Senses—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