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 21512200 of 2381 papers

TitleStatusHype
Distributed Document and Phrase Co-embeddings for Descriptive Clustering0
Distributed Representations of Geographically Situated Language0
Distributional Measures of Semantic Distance: A Survey0
Distributional Neural Networks for Automatic Resolution of Crossword Puzzles0
Distributional Semantic Concept Models for Entity Relation Discovery0
Distributional Semantics for Resolving Bridging Mentions0
Distributional vectors encode referential attributes0
DIT: Summarisation and Semantic Expansion in Evaluating Semantic Similarity0
Ditto: A Simple and Efficient Approach to Improve Sentence Embeddings0
Divide & Conquer for Entailment-aware Multi-hop Evidence Retrieval0
DKPro Similarity: An Open Source Framework for Text Similarity0
DKPro TC: A Java-based Framework for Supervised Learning Experiments on Textual Data0
DLS@CU at SemEval-2016 Task 1: Supervised Models of Sentence Similarity0
DLS@CU-CORE: A Simple Machine Learning Model of Semantic Textual Similarity0
DLS@CU: Sentence Similarity from Word Alignment and Semantic Vector Composition0
DLS@CU: Sentence Similarity from Word Alignment0
DOCAL - Vicomtech's Participation in the WMT16 Shared Task on Bilingual Document Alignment0
DOCK: Detecting Objects by transferring Common-sense Knowledge0
Do Cross Modal Systems Leverage Semantic Relationships?0
Doctoral Advisor or Medical Condition: Towards Entity-specific Rankings of Knowledge Base Properties [Extended Version]0
Document Valuation in LLM Summaries: A Cluster Shapley Approach0
Does Free Word Order Hurt? Assessing the Practical Lexical Function Model for Croatian0
Do LLMs Exhibit Human-Like Reasoning? Evaluating Theory of Mind in LLMs for Open-Ended Responses0
Domain-Relevant Embeddings for Medical Question Similarity0
Comparative analysis of word embeddings in assessing semantic similarity of complex sentences0
Don't Blame Distributional Semantics if it can't do Entailment0
Do Smaller Language Models Answer Contextualised Questions Through Memorisation Or Generalisation?0
Do We Need to Differentiate Negative Candidates Before Training a Neural Ranker?0
DTAFA: Decoupled Training Architecture for Efficient FAQ Retrieval0
DTSim at SemEval-2016 Task 1: Semantic Similarity Model Including Multi-Level Alignment and Vector-Based Compositional Semantics0
DTSim at SemEval-2016 Task 2: Interpreting Similarity of Texts Based on Automated Chunking, Chunk Alignment and Semantic Relation Prediction0
DT\_Team at SemEval-2017 Task 1: Semantic Similarity Using Alignments, Sentence-Level Embeddings and Gaussian Mixture Model Output0
Dual-Stream Knowledge-Preserving Hashing for Unsupervised Video Retrieval0
Dual Tensor Model for Detecting Asymmetric Lexico-Semantic Relations0
Duluth : Measuring Cross-Level Semantic Similarity with First and Second Order Dictionary Overlaps0
Duplicate Detection in a Knowledge Base with PIKA0
Dynamic Few-Shot Learning for Knowledge Graph Question Answering0
EASE: Entity-Aware Contrastive Learning of Sentence Embedding0
Ebiquity: Paraphrase and Semantic Similarity in Twitter using Skipgrams0
ECNU at SemEval-2016 Task 1: Leveraging Word Embedding From Macro and Micro Views to Boost Performance for Semantic Textual Similarity0
ECNU at SemEval-2016 Task 3: Exploring Traditional Method and Deep Learning Method for Question Retrieval and Answer Ranking in Community Question Answering0
ECNU at SemEval-2017 Task 1: Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity0
ECNU at SemEval-2017 Task 3: Using Traditional and Deep Learning Methods to Address Community Question Answering Task0
ECNU at SemEval-2017 Task 7: Using Supervised and Unsupervised Methods to Detect and Locate English Puns0
ECNU at SemEval-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection0
ECNUCS: Measuring Short Text Semantic Equivalence Using Multiple Similarity Measurements0
ECNUCS: Recognizing Cross-lingual Textual Entailment Using Multiple Text Similarity and Text Difference Measures0
ECNU: Leveraging on Ensemble of Heterogeneous Features and Information Enrichment for Cross Level Semantic Similarity Estimation0
ECNU: Leveraging Word Embeddings to Boost Performance for Paraphrase in Twitter0
ECNU: One Stone Two Birds: Ensemble of Heterogenous Measures for Semantic Relatedness and Textual Entailment0
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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
8ALBERTPearson Correlation0.93Unverified
9T5-11BPearson 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