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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

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Showing 1051–1100 of 2381 papers

TitleStatusHype
MNet-Sim: A Multi-layered Semantic Similarity Network to Evaluate Sentence Similarity—0
A text autoencoder from transformer for fast encoding language representation—0
Conditional Attention Networks for Distilling Knowledge Graphs in Recommendation—0
Assessing Effectiveness of Using Internal Signals for Check-Worthy Claim Identification in Unlabeled Data for Automated Fact-Checking—0
Effective and Imperceptible Adversarial Textual Attack via Multi-objectivizationCode0
A Deep Decomposable Model for Disentangling Syntax and Semantics in Sentence Representation—0
GiBERT: Enhancing BERT with Linguistic Information using a Lightweight Gated Injection MethodCode0
Locality Preserving Sentence Encoding—0
Searching for Legal Documents at Paragraph Level: Automating Label Generation and Use of an Extended Attention Mask for Boosting Neural Models of Semantic Similarity—0
Distilling Word Meaning in Context from Pre-trained Language ModelsCode0
A Fine-Grained Analysis of BERTScore—0
Semantic Alignment with Calibrated Similarity for Multilingual Sentence Embedding—0
Language-agnostic Representation from Multilingual Sentence Encoders for Cross-lingual Similarity EstimationCode0
Anaphora Resolution in Dialogue: Description of the DFKI-TalkingRobots System for the CODI-CRAC 2021 Shared-Task—0
Incorporating Semantic Textual Similarity and Lexical Matching for Information Retrieval—0
Cross-lingual Sentence Embedding using Multi-Task Learning—0
UnClE: Explicitly Leveraging Semantic Similarity to Reduce the Parameters of Word Embeddings—0
TransAug: Translate as Augmentation for Sentence Embeddings—0
Unsupervised Full Constituency Parsing with Neighboring Distribution Divergence—0
FacTeR-Check: Semi-automated fact-checking through Semantic Similarity and Natural Language Inference—0
MIC: Model-agnostic Integrated Cross-channel Recommenders—0
Two-stage Voice Application Recommender System for Unhandled Utterances in Intelligent Personal Assistant—0
FarFetched: An Entity-centric Approach for Reasoning on Textually Represented Environments—0
Doubly-Trained Adversarial Data Augmentation for Neural Machine TranslationCode0
Avoiding Forgetting and Allowing Forward Transfer in Continual Learning via Sparse NetworksCode0
An Isotropy Analysis in the Multilingual BERT Embedding SpaceCode0
Efficient Multi-Modal Embeddings from Structured Data—0
Exploiting Twitter as Source of Large Corpora of Weakly Similar Pairs for Semantic Sentence EmbeddingsCode0
Using Single-Trial Representational Similarity Analysis with EEG to track semantic similarity in emotional word processing—0
Contextualized Semantic Distance between Highly Overlapped TextsCode0
Neural sentence embedding models for semantic similarity estimation in the biomedical domainCode0
What Makes Better Augmentation Strategies? Augment Difficult but Not too Different—0
Agnostic Personalized Federated Learning with Kernel Factorization—0
Cluster Analysis with Deep Embeddings and Contrastive Learning—0
Sorting through the noise: Testing robustness of information processing in pre-trained language models—0
Rethinking Crowd Sourcing for Semantic Similarity—0
Towards Universal Dense Retrieval for Open-domain Question Answering—0
Investigating Entropy for Extractive Document Summarization—0
ConvFiT: Conversational Fine-Tuning of Pretrained Language Models—0
Adversarial Training with Contrastive Learning in NLP—0
Contrastive Word Embedding Learning for Neural Machine Translation—0
Transformers Can Compose Skills To Solve Novel Problems Without Finetuning—0
A Semantic Indexing Structure for Image Retrieval—0
MURAL: Multimodal, Multitask Retrieval Across Languages—0
Data Driven Content Creation using Statistical and Natural Language Processing Techniques for Financial Domain—0
On Length Divergence Bias in Textual Matching Models—0
PR-Net: Preference Reasoning for Personalized Video Highlight Detection—0
Assessing the Eligibility of Backtranslated Samples Based on Semantic Similarity for the Paraphrase Identification Task—0
Aligning Cross-lingual Sentence Representations with Dual Momentum Contrast—0
Evaluation Datasets for Cross-lingual Semantic Textual Similarity—0
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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
8T5-11BPearson Correlation0.93—Unverified
9ALBERTPearson 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