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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 751–800 of 2381 papers

TitleStatusHype
Complex Verbs are Different: Exploring the Visual Modality in Multi-Modal Models to Predict Compositionality—0
Comparison of Paragram and GloVe Results for Similarity Benchmarks—0
Assistive Completion of Agrammatic Aphasic Sentences: A Transfer Learning Approach using Neurolinguistics-based Synthetic Dataset—0
Comparison and Combination of Sentence Embeddings Derived from Different Supervision Signals—0
Comparing scalable strategies for generating numerical perspectives—0
Assessing the Eligibility of Backtranslated Samples Based on Semantic Similarity for the Paraphrase Identification Task—0
Comparing Data Sources and Architectures for Deep Visual Representation Learning in Semantics—0
Comparing Approaches for Automatic Question Identification—0
Assessing Effectiveness of Using Internal Signals for Check-Worthy Claim Identification in Unlabeled Data for Automated Fact-Checking—0
A Massive Scale Semantic Similarity Dataset of Historical English—0
Adapting VerbNet to French using existing resources—0
Comparing Apples to Apple: The Effects of Stemmers on Topic Models—0
Assessing Chinese Readability using Term Frequency and Lexical Chain—0
Community Search in Time-dependent Road-social Attributed Networks—0
Common Variable Learning and Invariant Representation Learning using Siamese Neural Networks—0
AspectCSE: Sentence Embeddings for Aspect-based Semantic Textual Similarity Using Contrastive Learning and Structured Knowledge—0
Combining Word Embeddings and N-grams for Unsupervised Document Summarization—0
A Spatial Model for Extracting and Visualizing Latent Discourse Structure in Text—0
Combining Structured and Unstructured Knowledge in an Interactive Search Dialogue System—0
Combining Contrastive Learning and Knowledge Graph Embeddings to develop medical word embeddings for the Italian language—0
ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity—0
ALOHa: A New Measure for Hallucination in Captioning Models—0
Adapting Sentence Transformers for the Aviation Domain—0
Accelerating LLaMA Inference by Enabling Intermediate Layer Decoding via Instruction Tuning with LITE—0
A Semi-supervised Multi-channel Graph Convolutional Network for Query Classification in E-commerce—0
Combinaison d'information visuelle, conceptuelle, et contextuelle pour la construction automatique de hierarchies semantiques adaptees a l'annotation d'images—0
Column sampling based discrete supervised hashing—0
Cognitively Motivated Distributional Representations of Meaning—0
A Siamese CNN Architecture for Learning Chinese Sentence Similarity—0
Cognate Identification using Machine Translation—0
A Short Answer Grading System in Chinese by Support Vector Approach—0
CogALex-V Shared Task: GHHH - Detecting Semantic Relations via Word Embeddings—0
Aligning Sentences from Standard Wikipedia to Simple Wikipedia—0
Adapting Dual-encoder Vision-language Models for Paraphrased Retrieval—0
Code Clone Detection based on Event Embedding and Event Dependency—0
A Sentence is Worth 128 Pseudo Tokens: A Semantic-Aware Contrastive Learning Framework for Sentence Embeddings—0
Co-Attention Hierarchical Network: Generating Coherent Long Distractors for Reading Comprehension—0
CNRC at SemEval-2016 Task 1: Experiments in Crosslingual Semantic Textual Similarity—0
A sense-based lexicon of count and mass expressions: The Bochum English Countability Lexicon—0
Aligning Cross-lingual Sentence Representations with Dual Momentum Contrast—0
CNGL: Grading Student Answers by Acts of Translation—0
CNGL-CORE: Referential Translation Machines for Measuring Semantic Similarity—0
ClusterLog: Clustering Logs for Effective Log-based Anomaly Detection—0
Align, Disambiguate and Walk: A Unified Approach for Measuring Semantic Similarity—0
AdapterSoup: Weight Averaging to Improve Generalization of Pretrained Language Models—0
Clustering Prominent People and Organizations in Topic-Specific Text Corpora—0
A Semantic Indexing Structure for Image Retrieval—0
Clustering and Diversifying Web Search Results with Graph-Based Word Sense Induction—0
Cluster Analysis with Deep Embeddings and Contrastive Learning—0
A Semantic Cover Approach for Topic Modeling—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