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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 1151–1200 of 2381 papers

TitleStatusHype
Predicting Human Metaphor Paraphrase Judgments with Deep Neural Networks—0
Predicting human similarity judgments with distributional models: The value of word associations.—0
Predicting Machine Translation Adequacy with Document Embeddings—0
Predicting Polarities of Tweets by Composing Word Embeddings with Long Short-Term Memory—0
Predicting the relevance of distributional semantic similarity with contextual information—0
Predicting the Score of Atomic Candidate OWL Class Axioms—0
Predicting the Semantic Textual Similarity with Siamese CNN and LSTM—0
Privacy Assessment on Reconstructed Images: Are Existing Evaluation Metrics Faithful to Human Perception?—0
PrivacyXray: Detecting Privacy Breaches in LLMs through Semantic Consistency and Probability Certainty—0
PR-Net: Preference Reasoning for Personalized Video Highlight Detection—0
Probabilistic Modeling of Joint-context in Distributional Similarity—0
Probabilistic Models of Cross-Lingual Semantic Similarity in Context Based on Latent Cross-Lingual Concepts Induced from Comparable Data—0
Probabilistic Sense Sentiment Similarity through Hidden Emotions—0
Probabilistic Soft Logic for Semantic Textual Similarity—0
Probabilistic Zero-shot Classification with Semantic Rankings—0
Probing Representations Learned by Multimodal Recurrent and Transformer Models—0
Problems With Evaluation of Word Embeddings Using Word Similarity Tasks—0
Proceedings of the LexSem+Logics Workshop 2016—0
Processing and Normalizing Hashtags—0
ProcSim: Proxy-based Confidence for Robust Similarity Learning—0
Product Feature Mining: Semantic Clues versus Syntactic Constituents—0
Promoting Semantics in Multi-objective Genetic Programming based on Decomposition—0
PromptBERT: Improving BERT Sentence Embeddings with Prompts—0
PromptExp: Multi-granularity Prompt Explanation of Large Language Models—0
Prompting Large Language Model for Machine Translation: A Case Study—0
Prompt Obfuscation for Large Language Models—0
Prompt-tuning for Clickbait Detection via Text Summarization—0
PropNet: a White-Box and Human-Like Network for Sentence Representation—0
ProtCLIP: Function-Informed Protein Multi-Modal Learning—0
ProtoGMM: Multi-prototype Gaussian-Mixture-based Domain Adaptation Model for Semantic Segmentation—0
ProtoGuard-guided PROPEL: Class-Aware Prototype Enhancement and Progressive Labeling for Incremental 3D Point Cloud Segmentation—0
PurdueNLP at SemEval-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings—0
Push for Quantization: Deep Fisher Hashing—0
QA4PRF: A Question Answering based Framework for Pseudo Relevance Feedback—0
Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT—0
QLUT at SemEval-2017 Task 1: Semantic Textual Similarity Based on Word Embeddings—0
QSTS: A Question-Sensitive Text Similarity Measure for Question Generation—0
Quality Estimation for Machine Translation Using the Joint Method of Evaluation Criteria and Statistical Modeling—0
Quantifying perturbation impacts for large language models—0
QU-BIGIR at SemEval 2017 Task 3: Using Similarity Features for Arabic Community Question Answering Forums—0
QurSim: A corpus for evaluation of relatedness in short texts—0
Qwant Research @DEFT 2019: Document matching and information retrieval using clinical cases—0
RAGulator: Lightweight Out-of-Context Detectors for Grounded Text Generation—0
Raising the Bar on the Evaluation of Out-of-Distribution Detection—0
RAMP: Retrieval and Attribute-Marking Enhanced Prompting for Attribute-Controlled Translation—0
Random Positive-Only Projections: PPMI-Enabled Incremental Semantic Space Construction—0
Random Walks and Neural Network Language Models on Knowledge Bases—0
Rank-Consistency Deep Hashing for Scalable Multi-Label Image Search—0
R&B: Domain Regrouping and Data Mixture Balancing for Efficient Foundation Model Training—0
Reasoning before Comparison: LLM-Enhanced Semantic Similarity Metrics for Domain Specialized Text Analysis—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