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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 1601–1650 of 2381 papers

TitleStatusHype
Learning Universal Sentence Embeddings with Large-scale Parallel Translation Datasets—0
Legal document retrieval across languages: topic hierarchies based on synsets—0
Legal Document Retrieval using Document Vector Embeddings and Deep Learning—0
Legal-ES: A Set of Large Scale Resources for Spanish Legal Text Processing—0
LegalGuardian: A Privacy-Preserving Framework for Secure Integration of Large Language Models in Legal Practice—0
Let Sense Bags Do Talking: Cross Lingual Word Semantic Similarity for English and Hindi—0
LEURN: Learning Explainable Univariate Rules with Neural Networks—0
Leveraging Crowdsourcing for Paraphrase Recognition—0
Leveraging LLMs to Enable Natural Language Search on Go-to-market Platforms—0
Leveraging Multi-lingual Positive Instances in Contrastive Learning to Improve Sentence Embedding—0
Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models—0
Lexical Access Preference and Constraint Strategies for Improving Multiword Expression Association within Semantic MT Evaluation—0
Lexical and Semantic Features for Cross-lingual Text Reuse Classification: an Experiment in English and Latin Paraphrases—0
Lexical Chains meet Word Embeddings in Document-level Statistical Machine Translation—0
Lexical Representation and Classification of Eventive Verbs - Polarity and Interaction between Process and State—0
Lexical semantics enhanced neural word embeddings—0
LICD: A Language-Independent Approach for Aspect Category Detection—0
Like a bilingual baby: The advantage of visually grounding a bilingual language model—0
Limbic: Author-Based Sentiment Aspect Modeling Regularized with Word Embeddings and Discourse Relations—0
LIM-LIG at SemEval-2017 Task1: Enhancing the Semantic Similarity for Arabic Sentences with Vectors Weighting—0
LIMSIILES: Basic English Substitution for Student Answer Assessment at SemEval 2013—0
LIMSI: Learning Semantic Similarity by Selecting Random Word Subsets—0
Linear Transformations for Cross-lingual Semantic Textual Similarity—0
LineRetriever: Planning-Aware Observation Reduction for Web Agents—0
Lingxi: A Diversity-aware Chinese Modern Poetry Generation System—0
Linking Entities Across Images and Text—0
Linking Symptom Inventories using Semantic Textual Similarity—0
Linking the Thoughts: Analysis of Argumentation Structures in Scientific Publications—0
LIPN-CORE: Semantic Text Similarity using n-grams, WordNet, Syntactic Analysis, ESA and Information Retrieval based Features—0
LIPN-IIMAS at SemEval-2016 Task 1: Random Forest Regression Experiments on Align-and-Differentiate and Word Embeddings penalizing strategies—0
LIPN-IIMAS at SemEval-2017 Task 1: Subword Embeddings, Attention Recurrent Neural Networks and Cross Word Alignment for Semantic Textual Similarity—0
LIPN: Introducing a new Geographical Context Similarity Measure and a Statistical Similarity Measure based on the Bhattacharyya coefficient—0
Literal or idiomatic? Identifying the reading of single occurrences of German multiword expressions using word embeddings—0
LlaMaVAE: Guiding Large Language Model Generation via Continuous Latent Sentence Spaces—0
LLMs as Better Recommenders with Natural Language Collaborative Signals: A Self-Assessing Retrieval Approach—0
LMSim : Computing Domain-specific Semantic Word Similarities Using a Language Modeling Approach—0
Locality Preserving Sentence Encoding—0
Looking for a Role for Word Embeddings in Eye-Tracking Features Prediction: Does Semantic Similarity Help?—0
Low-Dimensional Manifold Distributional Semantic Models—0
LSIS at SemEval-2017 Task 4: Using Adapted Sentiment Similarity Seed Words For English and Arabic Tweet Polarity Classification—0
LTV: Labeled Topic Vector—0
Lump at SemEval-2017 Task 1: Towards an Interlingua Semantic Similarity—0
Machine Learned Resume-Job Matching Solution—0
Machine Translation Evaluation using Bi-directional Entailment—0
Machine Translation Reference-less Evaluation using YiSi-2 with Bilingual Mappings of Massive Multilingual Language Model—0
Mahtab at SemEval-2017 Task 2: Combination of Corpus-based and Knowledge-based Methods to Measure Semantic Word Similarity—0
MALA: Cross-Domain Dialogue Generation with Action Learning—0
Manifold Learning-based Word Representation Refinement Incorporating Global and Local Information—0
MANTIS at TSAR-2022 Shared Task: Improved Unsupervised Lexical Simplification with Pretrained Encoders—0
Mapping it differently: A solution to the linking challenges—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