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 1526–1550 of 2381 papers

TitleStatusHype
Igevorse at SemEval-2018 Task 10: Exploring an Impact of Word Embeddings Concatenation for Capturing Discriminative Attributes—0
UniMelb at SemEval-2018 Task 12: Generative Implication using LSTMs, Siamese Networks and Semantic Representations with Synonym Fuzzing—0
ALANIS at SemEval-2018 Task 3: A Feature Engineering Approach to Irony Detection in English Tweets—0
ITNLP-ARC at SemEval-2018 Task 12: Argument Reasoning Comprehension with Attention—0
GKR: the Graphical Knowledge Representation for semantic parsing—0
SUNNYNLP at SemEval-2018 Task 10: A Support-Vector-Machine-Based Method for Detecting Semantic Difference using Taxonomy and Word Embedding FeaturesCode0
SemEval-2018 Task 10: Capturing Discriminative Attributes—0
THU\_NGN at SemEval-2018 Task 10: Capturing Discriminative Attributes with MLP-CNN model—0
IRCMS at SemEval-2018 Task 7 : Evaluating a basic CNN Method and Traditional Pipeline Method for Relation Classification—0
The Word Analogy Testing Caveat—0
NTU NLP Lab System at SemEval-2018 Task 10: Verifying Semantic Differences by Integrating Distributional Information and Expert Knowledge—0
Predicting Human Metaphor Paraphrase Judgments with Deep Neural Networks—0
Analogies in Complex Verb Meaning Shifts: the Effect of Affect in Semantic Similarity Models—0
UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes—0
UMD at SemEval-2018 Task 10: Can Word Embeddings Capture Discriminative Attributes?—0
Meaning\_space at SemEval-2018 Task 10: Combining explicitly encoded knowledge with information extracted from word embeddings—0
ABDN at SemEval-2018 Task 10: Recognising Discriminative Attributes using Context Embeddings and WordNet—0
Measuring Frame Instance Relatedness—0
Similarity Measures for the Detection of Clinical Conditions with Verbal Fluency Tasks—0
Phrase-Level Metaphor Identification Using Distributed Representations of Word Meaning—0
ECNU at SemEval-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection—0
Wolves at SemEval-2018 Task 10: Semantic Discrimination based on Knowledge and Association—0
Peperomia at SemEval-2018 Task 2: Vector Similarity Based Approach for Emoji Prediction—0
ALB at SemEval-2018 Task 10: A System for Capturing Discriminative Attributes—0
Specialising Word Vectors for Lexical EntailmentCode0
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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