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 16011650 of 2381 papers

TitleStatusHype
Sound Analogies with Phoneme Embeddings0
Detecting Language Impairments in Autism: A Computational Analysis of Semi-structured Conversations with Vector Semantics0
Evaluating the Stability of Embedding-based Word Similarities0
Learning Representations Specialized in Spatial Knowledge: Leveraging Language and VisionCode0
Beyond Word2Vec: Embedding Words and Phrases in Same Vector SpaceCode0
Soft Seeded SSL Graphs for Unsupervised Semantic Similarity-based Retrieval0
Learning Modality-Invariant Representations for Speech and Images0
Multimodal Visual Concept Learning with Weakly Supervised TechniquesCode0
Neural Networks for Semantic Textual Similarity0
SemTagger: A Novel Approach for Semantic Similarity Based Hashtag Recommendation on Twitter0
Suggesting Sentences for ESL using Kernel Embeddings0
Calculating Semantic Similarity between Academic Articles using Topic Event and Ontology0
Addressing Cross-Lingual Word Sense Disambiguation on Low-Density Languages: Application to Persian0
ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations0
Semantic Similarity Analysis for Paraphrase Identification in Arabic Texts0
SSAS: Semantic Similarity for Abstractive Summarization0
Are Manually Prepared Affective Lexicons Really Useful for Sentiment Analysis0
Semantic Features Based on Word Alignments for Estimating Quality of Text Simplification0
MIPA: Mutual Information Based Paraphrase Acquisition via Bilingual PivotingCode0
Hyperspherical Query Likelihood Models with Word Embeddings0
Turning Distributional Thesauri into Word Vectors for Synonym Extraction and Expansion0
The Challenge of Composition in Distributional and Formal Semantics0
Measuring Semantic Relations between Human Activities0
Testing the limits of unsupervised learning for semantic similarity0
A Semantically Motivated Approach to Compute ROUGE Scores0
Unsupervised Sentence Representations as Word Information Series: Revisiting TF--IDF0
Specialising Word Vectors for Lexical EntailmentCode0
Convolutional neural networks for structured omics: OmicsCNN and the OmicsConv layer0
A Semantic Relevance Based Neural Network for Text Summarization and Text SimplificationCode0
An\'alise de Medidas de Similaridade Sem\^antica na Tarefa de Reconhecimento de Implica \~ao Textual (Analysis of Semantic Similarity Measures in the Recognition of Textual Entailment Task)[In Portuguese]0
Avaliando a similaridade sem\^antica entre frases curtas atrav\'es de uma abordagem h\' (A hybrid approach to measure Semantic Textual Similarity between short sentences in Brazilian Portuguese)[In Portuguese]0
Bag-of-Vector Embeddings of Dependency Graphs for Semantic Induction0
An enhanced method to compute the similarity between concepts of ontology0
Retrofitting Concept Vector Representations of Medical Concepts to Improve Estimates of Semantic Similarity and Relatedness0
Doctoral Advisor or Medical Condition: Towards Entity-specific Rankings of Knowledge Base Properties [Extended Version]0
Think Globally, Embed Locally --- Locally Linear Meta-embedding of WordsCode0
Methodology and Results for the Competition on Semantic Similarity Evaluation and Entailment Recognition for PROPOR 20160
Using Summarization to Discover Argument Facets in Online Ideological Dialog0
Czech Dataset for Semantic Similarity and Relatedness0
Classifying Frames at the Sentence Level in News Articles0
Sentence-Level Multilingual Multi-modal Embedding for Natural Language Processing0
Parameter Transfer across Domains for Word Sense Disambiguation0
The Effect of Negative Sampling Strategy on Capturing Semantic Similarity in Document Embeddings0
Lexical Chains meet Word Embeddings in Document-level Statistical Machine Translation0
Learning to Score System Summaries for Better Content Selection Evaluation.0
Distractor Generation for Chinese Fill-in-the-blank Items0
Discovering Stylistic Variations in Distributional Vector Space Models via Lexical Paraphrases0
Exploring Vector Spaces for Semantic Relations0
Speaking, Seeing, Understanding: Correlating semantic models with conceptual representation in the brain0
Dual Tensor Model for Detecting Asymmetric Lexico-Semantic Relations0
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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