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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 1051–1100 of 2381 papers

TitleStatusHype
Automatic Thesaurus Construction for Modern Hebrew—0
Is Contrasting All You Need? Contrastive Learning for the Detection and Attribution of AI-generated Text—0
Deep Contextualized Pairwise Semantic Similarity for Arabic Language Questions—0
Automatic Real-word Error Correction in Persian Text—0
AI-enhanced semantic feature norms for 786 concepts—0
Automatic Pyramid Evaluation Exploiting EDU-based Extractive Reference Summaries—0
ISCAS\_NLP at SemEval-2016 Task 1: Sentence Similarity Based on Support Vector Regression using Multiple Features—0
Is Cosine-Similarity of Embeddings Really About Similarity?—0
Is this a Child, a Girl or a Car? Exploring the Contribution of Distributional Similarity to Learning Referential Word Meanings—0
ITNLP-AiKF at SemEval-2016 Task 3 a quesiton answering system using community QA repository—0
Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models—0
An Attention-Based Word-Level Interaction Model: Relation Detection for Knowledge Base Question Answering—0
Decoding Emotional Experiences in Dyadic Conversations of Married Couples: Leveraging Semantic Similarity through Sentence Embedding—0
Towards Automated Situation Awareness: A RAG-Based Framework for Peacebuilding Reports—0
Debiased Contrastive Learning of Unsupervised Sentence Representations—0
Investigating Entropy for Extractive Document Summarization—0
DebCSE: Rethinking Unsupervised Contrastive Sentence Embedding Learning in the Debiasing Perspective—0
Automatic Extraction of Turkish Hypernym-Hyponym Pairs From Large Corpus—0
DCU: Using Distributional Semantics and Domain Adaptation for the Semantic Textual Similarity SemEval-2015 Task 2—0
DCU-SEManiacs at SemEval-2016 Task 1: Synthetic Paragram Embeddings for Semantic Textual Similarity—0
Automatic Difficulty Classification of Arabic Sentences—0
Adversarial Contrastive Learning by Permuting Cluster Assignments—0
Investigating Large Language Models for Financial Causality Detection in Multilingual Setup—0
Automatic Detection of Cognates Using Orthographic Alignment—0
A Cognitive Study on Semantic Similarity Analysis of Large Corpora: A Transformer-based Approach—0
Data-driven Paraphrasing and Stylistic Harmonization—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
Investigating Antigram Behaviour using Distributional Semantics—0
Data Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase—0
DALR: Dual-level Alignment Learning for Multimodal Sentence Representation Learning—0
DalGTM at SemEval-2016 Task 1: Importance-Aware Compositional Approach to Short Text Similarity—0
D2CSE: Difference-aware Deep continuous prompts for Contrastive Sentence Embeddings—0
Automatically Extracting Topical Components for a Response-to-Text Writing Assessment—0
An Analytical Study of Synonymy in Assamese Language Using WorldNet: Classification and Structure—0
Investigating Continual Pretraining in Large Language Models: Insights and Implications—0
Investigating the Role of Negatives in Contrastive Representation Learning—0
Czech News Dataset for Semantic Textual Similarity—0
Czech Dataset for Semantic Similarity and Relatedness—0
Cyc3D: Fine-grained Controllable 3D Generation via Cycle Consistency Regularization—0
Xu: An Automated Query Expansion and Optimization Tool—0
An analysis of textual inference in German customer emails—0
Automated Feedback Loops to Protect Text Simplification with Generative AI from Information Loss—0
Cross-modal Subspace Learning via Kernel Correlation Maximization and Discriminative Structure Preserving—0
Analyzing Zero-shot Cross-lingual Transfer in Supervised NLP Tasks—0
Cross-modal Deep Metric Learning with Multi-task Regularization—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
Introducing two Vietnamese Datasets for Evaluating Semantic Models of (Dis-)Similarity and Relatedness—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