Semantic Textual Similarity
560 papers with code • 13 benchmarks • 17 datasets
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
Benchmarks
These leaderboards are used to track progress in Semantic Textual Similarity
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Libraries
Use these libraries to find Semantic Textual Similarity models and implementations
GLUE
MRPC
SICK
SentEval
CARER
MTEB
STS Benchmark
EVALution
Latest papers
Semantic Textual Similarity Assessment in Chest X-ray Reports Using a Domain-Specific Cosine-Based Metric
Medical language processing and deep learning techniques have emerged as critical tools for improving healthcare, particularly in the analysis of medical imaging and medical text data.
SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 14 Languages
semantic-textual-relatedness/semantic_relatedness_semeval2024
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Exploring and quantifying semantic relatedness is central to representing language.
Pixel Sentence Representation Learning
gowitheflow-1998/pixel-linguist
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To our knowledge, this is the first representation learning method devoid of traditional language models for understanding sentence and document semantics, marking a stride closer to human-like textual comprehension.
OrderBkd: Textual backdoor attack through repositioning
The use of third-party datasets and pre-trained machine learning models poses a threat to NLP systems due to possibility of hidden backdoor attacks.
HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on Text
hqa-attack/hqaattack-demo
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NeurIPS 2023
Black-box hard-label adversarial attack on text is a practical and challenging task, as the text data space is inherently discrete and non-differentiable, and only the predicted label is accessible.
Benchmarking Transferable Adversarial Attacks
The robustness of deep learning models against adversarial attacks remains a pivotal concern.
DenoSent: A Denoising Objective for Self-Supervised Sentence Representation Learning
These methods regularize the representation space by pulling similar sentence representations closer and pushing away the dissimilar ones and have been proven effective in various NLP tasks, e. g., semantic textual similarity (STS) tasks.
Contrastive Learning in Distilled Models
kennethlimjf/contrastive-learning-in-distilled-models
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Natural Language Processing models like BERT can provide state-of-the-art word embeddings for downstream NLP tasks.
Noise Contrastive Estimation-based Matching Framework for Low-Resource Security Attack Pattern Recognition
Tactics, Techniques and Procedures (TTPs) represent sophisticated attack patterns in the cybersecurity domain, described encyclopedically in textual knowledge bases.
A character-based steganography using masked language modeling
emirozturk/MLMStego
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IEEE Access 2024
In this study, a steganography method based on BERT transformer model is proposed for hiding text data in cover text.
