SOTAVerified

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

Showing 12511300 of 2381 papers

TitleStatusHype
TSI: an Ad Text Strength Indicator using Text-to-CTR and Semantic-Ad-Similarity0
tucSage: Grammar Rule Induction for Spoken Dialogue Systems via Probabilistic Candidate Selection0
Turning Distributional Thesauri into Word Vectors for Synonym Extraction and Expansion0
Tweet2Vec: Learning Tweet Embeddings Using Character-level CNN-LSTM Encoder-Decoder0
Twitter-based traffic information system based on vector representations for words0
Twitter Paraphrase Identification with Simple Overlap Features and SVMs0
Two-stage Voice Application Recommender System for Unhandled Utterances in Intelligent Personal Assistant0
Type Information Utilized Event Detection via Multi-Channel GNNs in Electrical Power Systems0
Typology of Adjectives Benchmark for Compositional Distributional Models0
UAlacant: Using Online Machine Translation for Cross-Lingual Textual Entailment0
UBC: Cubes for English Semantic Textual Similarity and Supervised Approaches for Interpretable STS0
UCAM-CORE: Incorporating structured distributional similarity into STS0
uHelp: intelligent volunteer search for mutual help communities0
UINSUSKA-TiTech at SemEval-2017 Task 3: Exploiting Word Importance Levels for Similarity Features for CQA0
UKP-BIU: Similarity and Entailment Metrics for Student Response Analysis0
UKP: Computing Semantic Textual Similarity by Combining Multiple Content Similarity Measures0
UMBC\_EBIQUITY-CORE: Semantic Textual Similarity Systems0
UMCC\_DLSI: Multidimensional Lexical-Semantic Textual Similarity0
UMCC\_DLSI\_SemSim: Multilingual System for Measuring Semantic Textual Similarity0
UMCC\_DLSI: Textual Similarity based on Lexical-Semantic features0
UMD at SemEval-2018 Task 10: Can Word Embeddings Capture Discriminative Attributes?0
UMDeep at SemEval-2017 Task 1: End-to-End Shared Weight LSTM Model for Semantic Textual Similarity0
UMD-TTIC-UW at SemEval-2016 Task 1: Attention-Based Multi-Perspective Convolutional Neural Networks for Textual Similarity Measurement0
UMDuluth-BlueTeam: SVCSTS - A Multilingual and Chunk Level Semantic Similarity System0
UMLS::Similarity: Measuring the Relatedness and Similarity of Biomedical Concepts0
UNAL-NLP: Combining Soft Cardinality Features for Semantic Textual Similarity, Relatedness and Entailment0
Unbiased Sentence Encoder For Large-Scale Multi-lingual Search Engines0
UNBNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation0
UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes0
Uncertainty-based Visual Question Answering: Estimating Semantic Inconsistency between Image and Knowledge Base0
Uncertainty-based Visual Question Answering: Estimating Semantic Inconsistency between Image and Knowledge Base0
Uncertainty Quantification of Large Language Models through Multi-Dimensional Responses0
UnClE: Explicitly Leveraging Semantic Similarity to Reduce the Parameters of Word Embeddings0
Uncovering Semantics and Topics Utilized by Threat Actors to Deliver Malicious Attachments and URLs0
Understanding Idiomatic Variation0
UNED: Improving Text Similarity Measures without Human Assessments0
UniAdapt: A Universal Adapter for Knowledge Calibration0
UNIBA: Combining Distributional Semantic Models and Word Sense Disambiguation for Textual Similarity0
UNIBA-CORE: Combining Strategies for Semantic Textual Similarity0
UNIBA: Distributional Semantics for Textual Similarity0
Unifying Demonstration Selection and Compression for In-Context Learning0
Unifying Specialist Image Embedding into Universal Image Embedding0
UniMelb at SemEval-2016 Task 3: Identifying Similar Questions by combining a CNN with String Similarity Measures0
UniMelb at SemEval-2018 Task 12: Generative Implication using LSTMs, Siamese Networks and Semantic Representations with Synonym Fuzzing0
UniMelb\_NLP-CORE: Integrating predictions from multiple domains and feature sets for estimating semantic textual similarity0
UNITOR: Combining Semantic Text Similarity functions through SV Regression0
UNITOR-CORE\_TYPED: Combining Text Similarity and Semantic Filters through SV Regression0
Universal Conceptual Cognitive Annotation (UCCA)0
Universal Correspondence Network0
Universal Features Guided Zero-Shot Category-Level Object Pose Estimation0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SMARTRoBERTaDev Pearson Correlation92.8Unverified
2DeBERTa (large)Accuracy92.5Unverified
3SMART-BERTDev Pearson Correlation90Unverified
4MT-DNN-SMARTPearson Correlation0.93Unverified
5StructBERTRoBERTa ensemblePearson Correlation0.93Unverified
6Mnet-SimPearson Correlation0.93Unverified
7XLNet (single model)Pearson Correlation0.93Unverified
8T5-11BPearson Correlation0.93Unverified
9ALBERTPearson Correlation0.93Unverified
10RoBERTaPearson Correlation0.92Unverified
#ModelMetricClaimedVerifiedStatus
1AnglE-UAESpearman Correlation84.54Unverified
2ST5-XXLSpearman Correlation82.63Unverified
3ST5-LargeSpearman Correlation81.83Unverified
4ST5-XLSpearman Correlation81.66Unverified
5ST5-BaseSpearman Correlation81.14Unverified
6MPNet-multilingualSpearman Correlation80.73Unverified
7SGPT-5.8B-nliSpearman Correlation80.53Unverified
8MPNetSpearman Correlation80.28Unverified
9MiniLM-L12Spearman Correlation79.8Unverified
10SimCSE-BERT-supSpearman Correlation79.12Unverified
#ModelMetricClaimedVerifiedStatus
1MT-DNN-SMARTAccuracy93.7Unverified
2ALBERTAccuracy93.4Unverified
3RoBERTa (ensemble)Accuracy92.3Unverified
4BigBirdF191.5Unverified
5StructBERTRoBERTa ensembleAccuracy91.5Unverified
6FLOATER-largeAccuracy91.4Unverified
7SMARTAccuracy91.3Unverified
8RoBERTa-large 355M (MLP quantized vector-wise, fine-tuned)Accuracy91Unverified
9RoBERTa-large 355M + Entailment as Few-shot LearnerF191Unverified
10SpanBERTAccuracy90.9Unverified
#ModelMetricClaimedVerifiedStatus
1PromCSE-RoBERTa-large (0.355B)Spearman Correlation0.82Unverified
2PromptEOL+CSE+LLaMA-30BSpearman Correlation0.82Unverified
3PromptEOL+CSE+OPT-13BSpearman Correlation0.82Unverified
4SimCSE-RoBERTalargeSpearman Correlation0.82Unverified
5PromptEOL+CSE+OPT-2.7BSpearman Correlation0.81Unverified
6SentenceBERTSpearman Correlation0.75Unverified
7SRoBERTa-NLI-baseSpearman Correlation0.74Unverified
8SRoBERTa-NLI-largeSpearman Correlation0.74Unverified
9Dino (STS/̄🦕)Spearman Correlation0.74Unverified
10SBERT-NLI-largeSpearman Correlation0.74Unverified
#ModelMetricClaimedVerifiedStatus
1AnglE-LLaMA-7BSpearman Correlation0.91Unverified
2AnglE-LLaMA-7B-v2Spearman Correlation0.91Unverified
3PromptEOL+CSE+LLaMA-30BSpearman Correlation0.9Unverified
4PromptEOL+CSE+OPT-13BSpearman Correlation0.9Unverified
5PromptEOL+CSE+OPT-2.7BSpearman Correlation0.9Unverified
6PromCSE-RoBERTa-large (0.355B)Spearman Correlation0.89Unverified
7Trans-Encoder-BERT-large-bi (unsup.)Spearman Correlation0.89Unverified
8Trans-Encoder-BERT-large-cross (unsup.)Spearman Correlation0.88Unverified
9Trans-Encoder-RoBERTa-large-cross (unsup.)Spearman Correlation0.88Unverified
10SimCSE-RoBERTa-largeSpearman Correlation0.87Unverified