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Found 9 papers, 0 papers with code
Document Automation Architectures: Updated Survey in Light of Large Language Models
This paper surveys the current state of the art in document automation (DA).
Understanding Metrics for Paraphrasing
This is not only because of the limitations in text generation capabilities but also due that to the lack of a proper definition of what qualifies as a paraphrase and corresponding metrics to measure how good it is.
Pruning Attention Heads of Transformer Models Using A* Search: A Novel Approach to Compress Big NLP Architectures
Our results indicate that the method could eliminate as much as 40% of the attention heads in the BERT transformer model with no loss in accuracy.
Document Automation Architectures and Technologies: A Survey
This paper surveys the current state of the art in document automation (DA).
Self-interpretable Convolutional Neural Networks for Text Classification
We also study the impact of the complexity of the convolutional layers and the classification layers on the model performance.
Robustness Tests of NLP Machine Learning Models: Search and Semantically Replace
This paper proposes a strategy to assess the robustness of different machine learning models that involve natural language processing (NLP).
Recent Trends in the Use of Deep Learning Models for Grammar Error Handling
Additionally, we discuss different techniques to improve the performance of these models at each stage of the pipeline.
SHAP values for Explaining CNN-based Text Classification Models
Deep neural networks are increasingly used in natural language processing (NLP) models.
Model Robustness with Text Classification: Semantic-preserving adversarial attacks
We propose algorithms to create adversarial attacks to assess model robustness in text classification problems.
