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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 1851–1900 of 2381 papers

TitleStatusHype
A Semantically Enhanced Approach to Determine Textual Similarity—0
A Semantically Motivated Approach to Compute ROUGE Scores—0
A Semantic Cover Approach for Topic Modeling—0
A Semantic Indexing Structure for Image Retrieval—0
A sense-based lexicon of count and mass expressions: The Bochum English Countability Lexicon—0
A Sentence is Worth 128 Pseudo Tokens: A Semantic-Aware Contrastive Learning Framework for Sentence Embeddings—0
A Short Answer Grading System in Chinese by Support Vector Approach—0
A Siamese CNN Architecture for Learning Chinese Sentence Similarity—0
ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity—0
A Spatial Model for Extracting and Visualizing Latent Discourse Structure in Text—0
AspectCSE: Sentence Embeddings for Aspect-based Semantic Textual Similarity Using Contrastive Learning and Structured Knowledge—0
Assessing Chinese Readability using Term Frequency and Lexical Chain—0
Assessing Effectiveness of Using Internal Signals for Check-Worthy Claim Identification in Unlabeled Data for Automated Fact-Checking—0
Assessing the Eligibility of Backtranslated Samples Based on Semantic Similarity for the Paraphrase Identification Task—0
Assistive Completion of Agrammatic Aphasic Sentences: A Transfer Learning Approach using Neurolinguistics-based Synthetic Dataset—0
Associative and Semantic Features Extracted From Web-Harvested Corpora—0
A Structured Distributional Semantic Model for Event Co-reference—0
A Structured Distributional Semantic Model : Integrating Structure with Semantics—0
A Study of Heterogeneous Similarity Measures for Semantic Relation Extraction—0
A Study of Hybrid Similarity Measures for Semantic Relation Extraction—0
A Study of Metrics of Distance and Correlation Between Ranked Lists for Compositionality Detection—0
A Summariser based on Human Memory Limitations and Lexical Competition—0
A Survey on Automatically-Constructed WordNets and their Evaluation: Lexical and Word Embedding-based Approaches—0
A Survey on Text Simplification—0
A Systematic Study of Semantic Vector Space Model Parameters—0
ATA-Sem: Chunk-based Determination of Semantic Text Similarity—0
ATEB: Evaluating and Improving Advanced NLP Tasks for Text Embedding Models—0
A text autoencoder from transformer for fast encoding language representation—0
A Text is Worth Several Tokens: Text Embedding from LLMs Secretly Aligns Well with The Key Tokens—0
A Theory-Driven Self-Labeling Refinement Method for Contrastive Representation Learning—0
A Thesaurus for Biblical Hebrew—0
Attention-aware semantic relevance predicting Chinese sentence reading—0
Attention-based Cross-Layer Domain Alignment for Unsupervised Domain Adaptation—0
Attention Discriminant Sampling for Point Clouds—0
Attribute-Graph: A Graph based approach to Image Ranking—0
Augmentable Paraphrase Extraction Framework—0
Augmenting Modelers with Semantic Autocompletion of Processes—0
A Unified Kernel Approach for Learning Typed Sentence Rewritings—0
A Unified Multilingual Semantic Representation of Concepts—0
Author-aware Aspect Topic Sentiment Model to Retrieve Supporting Opinions from Reviews—0
AuthorMist: Evading AI Text Detectors with Reinforcement Learning—0
AutoBERT-Zero: Evolving BERT Backbone from Scratch—0
Autoencoder-Based Domain Learning for Semantic Communication with Conceptual Spaces—0
Autoencoding Low-Resolution MRI for Semantically Smooth Interpolation of Anisotropic MRI—0
Automated Assessment of Students' Code Comprehension using LLMs—0
Automated Fact-Checking of Claims in Argumentative Parliamentary Debates—0
Automated Feedback Loops to Protect Text Simplification with Generative AI from Information Loss—0
Xu: An Automated Query Expansion and Optimization Tool—0
Automatically Extracting Topical Components for a Response-to-Text Writing Assessment—0
Automatic Detection of Cognates Using Orthographic Alignment—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