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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2301–2350 of 2381 papers

TitleStatusHype
Exploring the use of word embeddings and random walks on Wikipedia for the CogAlex shared task—0
Exploring Vector Spaces for Semantic Relations—0
Exploring Word Embeddings for Unsupervised Textual User-Generated Content Normalization—0
Exponential Family Embeddings—0
Probing Cross-Lingual Lexical Knowledge from Multilingual Sentence Encoders—0
On Length Divergence Bias in Textual Matching Models—0
Expressions of Anxiety in Political Texts—0
Extending Monolingual Semantic Textual Similarity Task to Multiple Cross-lingual Settings—0
Extending WordNet with Fine-Grained Collocational Information via Supervised Distributional Learning—0
Extracting Sentence Embeddings from Pretrained Transformer Models—0
Extrapolating Binder Style Word Embeddings to New Words—0
FacTeR-Check: Semi-automated fact-checking through Semantic Similarity and Natural Language Inference—0
FaMTEB: Massive Text Embedding Benchmark in Persian Language—0
FarFetched: An Entity-centric Approach for Reasoning on Textually Represented Environments—0
Farmer-Bot: An Interactive Bot for Farmers—0
FarSSiBERT: A Novel Transformer-based Model for Semantic Similarity Measurement of Persian Social Networks Informal Texts—0
Fast and Easy Short Answer Grading with High Accuracy—0
FAST-Splat: Fast, Ambiguity-Free Semantics Transfer in Gaussian Splatting—0
FBK-HLT: An Application of Semantic Textual Similarity for Answer Selection in Community Question Answering—0
FBK-HLT: An Effective System for Paraphrase Identification and Semantic Similarity in Twitter—0
FBK-HLT: A New Framework for Semantic Textual Similarity—0
FBK HLT-MT at SemEval-2016 Task 1: Cross-lingual Semantic Similarity Measurement Using Quality Estimation Features and Compositional Bilingual Word Embeddings—0
FBK-HLT-NLP at SemEval-2016 Task 2: A Multitask, Deep Learning Approach for Interpretable Semantic Textual Similarity—0
FBK: Machine Translation Evaluation and Word Similarity metrics for Semantic Textual Similarity—0
FBK-TR: Applying SVM with Multiple Linguistic Features for Cross-Level Semantic Similarity—0
FCC: Three Approaches for Semantic Textual Similarity—0
FCICU at SemEval-2017 Task 1: Sense-Based Language Independent Semantic Textual Similarity Approach—0
FCICU: The Integration between Sense-Based Kernel and Surface-Based Methods to Measure Semantic Textual Similarity—0
Feature Engineering in Learning-to-Rank for Community Question Answering Task—0
FedDTPT: Federated Discrete and Transferable Prompt Tuning for Black-Box Large Language Models—0
Feedback-Aware Monte Carlo Tree Search for Efficient Information Seeking in Goal-Oriented Conversations—0
Feedforward Legendre Memory Unit—0
Feeling is Understanding: From Affective to Semantic Spaces—0
Few-Shot Joint Multimodal Entity-Relation Extraction via Knowledge-Enhanced Cross-modal Prompt Model—0
Few-shot Named Entity Recognition with Joint Token and Sentence Awareness—0
Figure Me Out: A Gold Standard Dataset for Metaphor Interpretation—0
FILM: A Fast, Interpretable, and Low-rank Metric Learning Approach for Sentence Matching—0
Filter and Match Approach to Pair-wise Web URI Linking—0
Finding Salient Context based on Semantic Matching for Relevance Ranking—0
Finding the Topic of a Set of Images—0
FindMeIfYouCan: Bringing Open Set metrics to near , far and farther Out-of-Distribution Object Detection—0
Fine-Grained ECG-Text Contrastive Learning via Waveform Understanding Enhancement—0
Fine-Grained Guidance for Retrievers: Leveraging LLMs' Feedback in Retrieval-Augmented Generation—0
Fine-grained Semantic Textual Similarity for Serbian—0
Fine-tuning CLIP Text Encoders with Two-step Paraphrasing—0
FlowDreamer: A RGB-D World Model with Flow-based Motion Representations for Robot Manipulation—0
Fountain -- an intelligent contextual assistant combining knowledge representation and language models for manufacturing risk identification—0
Frequency-based Distortions in Contextualized Word Embeddings—0
Frequently Asked Questions Retrieval for Croatian Based on Semantic Textual Similarity—0
Friend Recommendation based on Hashtags Analysis—0
Show:102550
← PrevPage 47 of 48Next →
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