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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 901–950 of 2381 papers

TitleStatusHype
Explicit Retrofitting of Distributional Word Vectors—0
Fine-Grained Guidance for Retrievers: Leveraging LLMs' Feedback in Retrieval-Augmented Generation—0
Explicit Pairwise Word Interaction Modeling Improves Pretrained Transformers for English Semantic Similarity Tasks—0
Fine-tuning CLIP Text Encoders with Two-step Paraphrasing—0
Explanations for CommonsenseQA: New Dataset and Models—0
Check-Eval: A Checklist-based Approach for Evaluating Text Quality—0
ExB Themis: Extensive Feature Extraction from Word Alignments for Semantic Textual Similarity—0
Examining the robustness of LLM evaluation to the distributional assumptions of benchmarks—0
Chasing Hypernyms in Vector Spaces with Entropy—0
Argument extraction for supporting public policy formulation—0
Fountain -- an intelligent contextual assistant combining knowledge representation and language models for manufacturing risk identification—0
Frequency-based Distortions in Contextualized Word Embeddings—0
A corpus-based evaluation method for Distributional Semantic Models—0
Frequently Asked Questions Retrieval for Croatian Based on Semantic Textual Similarity—0
Friend Recommendation based on Hashtags Analysis—0
FrNewsLink : a corpus linking TV Broadcast News Segments and Press Articles—0
From Disjoint Sets to Parallel Data to Train Seq2Seq Models for Sentiment Transfer—0
From distributional semantics to feature norms: grounding semantic models in human perceptual data—0
Examining Multilingual Embedding Models Cross-Lingually Through LLM-Generated Adversarial Examples—0
From Interoperable Annotations towards Interoperable Resources: A Multilingual Approach to the Analysis of Discourse—0
Evolving the MCTS Upper Confidence Bounds for Trees Using a Semantic-inspired Evolutionary Algorithm in the Game of Carcassonne—0
Evolution of Semantic Similarity -- A Survey—0
Evolutionary Algorithms Approach For Search Based On Semantic Document Similarity—0
Characters or Morphemes: How to Represent Words?—0
Fully Transformer-Equipped Architecture for End-to-End Referring Video Object Segmentation—0
Fully Unsupervised Crosslingual Semantic Textual Similarity Metric Based on BERT for Identifying Parallel Data—0
EviNets: Neural Networks for Combining Evidence Signals for Factoid Question Answering—0
Everyone deserves their voice to be heard: Analyzing Predictive Gender Bias in ASR Models Applied to Dutch Speech Data—0
Event Segmentation Applications in Large Language Model Enabled Automated Recall Assessments—0
Event Detection and Co-reference with Minimal Supervision—0
Character Set Construction for Chinese Language Learning—0
GeAR: Generation Augmented Retrieval—0
Are Word Embedding-based Features Useful for Sarcasm Detection?—0
Generalised Differential Privacy for Text Document Processing—0
EVALution 1.0: an Evolving Semantic Dataset for Training and Evaluation of Distributional Semantic Models—0
Generalising and Normalising Distributional Contexts to Reduce Data Sparsity: Application to Medical Corpora—0
Evaluation on Second Language Collocational Congruency with Computational Semantic Similarity—0
Evaluation of taxonomic and neural embedding methods for calculating semantic similarity—0
Evaluation of Simple Distributional Compositional Operations on Longer Texts—0
CFILT-CORE: Semantic Textual Similarity using Universal Networking Language—0
Evaluation of Semantic Search and its Role in Retrieved-Augmented-Generation (RAG) for Arabic Language—0
Evaluation of BERT and ALBERT Sentence Embedding Performance on Downstream NLP Tasks—0
Evaluation Datasets for Cross-lingual Semantic Textual Similarity—0
Evaluation by Association: A Systematic Study of Quantitative Word Association Evaluation—0
Center-wise Local Image Mixture For Contrastive Representation Learning—0
Generative Semantic Communication for Joint Image Transmission and Segmentation—0
Are We Truly Forgetting? A Critical Re-examination of Machine Unlearning Evaluation Protocols—0
GETALP System : Propagation of a Lesk Measure through an Ant Colony Algorithm—0
Gextext: Disease Network Extraction from Biomedical Literature—0
A Large-Scale Multilingual Disambiguation of Glosses—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