Sentence Embeddings
220 papers with code • 0 benchmarks • 11 datasets
Benchmarks
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Libraries
Use these libraries to find Sentence Embeddings models and implementationsDatasets
WikiMatrix
ScanRefer Dataset
Discovery
SemEval-2014 Task-10
Subtasks
Latest papers with no code
Self-Adaptive Reconstruction with Contrastive Learning for Unsupervised Sentence Embeddings
However, due to the token bias in pretrained language models, the models can not capture the fine-grained semantics in sentences, which leads to poor predictions.
Improving Sentence Embeddings with an Automatically Generated NLI Dataset
Decoder-based large language models (LLMs) have shown high performance on many tasks in natural language processing.
2D Matryoshka Sentence Embeddings
The experimental results demonstrate the effectiveness of our proposed model in dynamically supporting different embedding sizes and Transformer layers, allowing it to be highly adaptable to various scenarios.
Are ELECTRA's Sentence Embeddings Beyond Repair? The Case of Semantic Textual Similarity
While BERT produces high-quality sentence embeddings, its pre-training computational cost is a significant drawback.
Ontology Enhanced Claim Detection
We fused ontology embeddings from a knowledge base with BERT sentence embeddings to perform claim detection for the ClaimBuster and the NewsClaims datasets.
RankSum An unsupervised extractive text summarization based on rank fusion
In this paper, we propose Ranksum, an approach for extractive text summarization of single documents based on the rank fusion of four multi-dimensional sentence features extracted for each sentence: topic information, semantic content, significant keywords, and position.
LSTM-based Deep Neural Network With A Focus on Sentence Representation for Sequential Sentence Classification in Medical Scientific Abstracts
For this reason, the role of sentence embedding is crucial for capturing both the semantic information between words in the sentence and the contextual relationship of sentences within the abstract to provide a comprehensive representation for better classification.
A Comprehensive View of the Biases of Toxicity and Sentiment Analysis Methods Towards Utterances with African American English Expressions
One explanation for this bias is that AI models are trained on limited datasets, and using such a term in training data is more likely to appear in a toxic utterance.
Hierarchical Classification of Transversal Skills in Job Ads Based on Sentence Embeddings
Thus, a new approach is proposed for the hierarchical classification of transversal skills from job ads.
Disentangling continuous and discrete linguistic signals in transformer-based sentence embeddings
We explore whether we can compress transformer-based sentence embeddings into a representation that separates different linguistic signals -- in particular, information relevant to subject-verb agreement and verb alternations.
