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 15011550 of 2381 papers

TitleStatusHype
GenSense: A Generalized Sense Retrofitting ModelCode0
LTV: Labeled Topic Vector0
SetExpander: End-to-end Term Set Expansion Based on Multi-Context Term Embeddings0
Effective Parallel Corpus Mining using Bilingual Sentence Embeddings0
Clustering Prominent People and Organizations in Topic-Specific Text Corpora0
Large-Scale Multi-Domain Belief Tracking with Knowledge SharingCode0
Pangloss: Fast Entity Linking in Noisy Text Environments0
Linear Transformations for Cross-lingual Semantic Textual Similarity0
A Short Answer Grading System in Chinese by Support Vector Approach0
Using pseudo-senses for improving the extraction of synonyms from word embeddings0
Explicit Retrofitting of Distributional Word Vectors0
A Spatial Model for Extracting and Visualizing Latent Discourse Structure in Text0
Injecting Lexical Contrast into Word Vectors by Guiding Vector Space Specialisation0
Learning Distributional Token Representations from Visual Features0
Learning Thematic Similarity Metric from Article Sections Using Triplet Networks0
Exploring Semantic Properties of Sentence Embeddings0
WordNet EmbeddingsCode0
Characters or Morphemes: How to Represent Words?0
Learning-based Composite Metrics for Improved Caption Evaluation0
Are BLEU and Meaning Representation in Opposition?0
Paragraph-based complex networks: application to document classification and authenticity verification0
The Corpus Replication Task0
Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question AnsweringCode0
Deploying Deep Ranking Models for Search Verticals0
Semi-Supervised Clustering with Neural Networks0
Igevorse at SemEval-2018 Task 10: Exploring an Impact of Word Embeddings Concatenation for Capturing Discriminative Attributes0
UniMelb at SemEval-2018 Task 12: Generative Implication using LSTMs, Siamese Networks and Semantic Representations with Synonym Fuzzing0
ALANIS at SemEval-2018 Task 3: A Feature Engineering Approach to Irony Detection in English Tweets0
ITNLP-ARC at SemEval-2018 Task 12: Argument Reasoning Comprehension with Attention0
GKR: the Graphical Knowledge Representation for semantic parsing0
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 Attributes0
THU\_NGN at SemEval-2018 Task 10: Capturing Discriminative Attributes with MLP-CNN model0
IRCMS at SemEval-2018 Task 7 : Evaluating a basic CNN Method and Traditional Pipeline Method for Relation Classification0
The Word Analogy Testing Caveat0
NTU NLP Lab System at SemEval-2018 Task 10: Verifying Semantic Differences by Integrating Distributional Information and Expert Knowledge0
Predicting Human Metaphor Paraphrase Judgments with Deep Neural Networks0
Analogies in Complex Verb Meaning Shifts: the Effect of Affect in Semantic Similarity Models0
UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes0
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 embeddings0
ABDN at SemEval-2018 Task 10: Recognising Discriminative Attributes using Context Embeddings and WordNet0
Measuring Frame Instance Relatedness0
Similarity Measures for the Detection of Clinical Conditions with Verbal Fluency Tasks0
Phrase-Level Metaphor Identification Using Distributed Representations of Word Meaning0
ECNU at SemEval-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection0
Wolves at SemEval-2018 Task 10: Semantic Discrimination based on Knowledge and Association0
Peperomia at SemEval-2018 Task 2: Vector Similarity Based Approach for Emoji Prediction0
ALB at SemEval-2018 Task 10: A System for Capturing Discriminative Attributes0
Specialising Word Vectors for Lexical EntailmentCode0
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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