Core Concepts
Advanced language models like BART and MarianMT are effective in correcting spelling and grammatical errors in text documents.
Abstract
The content discusses the use of advanced deep neural network-based language models, BART and MarianMT, to rectify errors in text documents. It explores error categories, model training, dataset analysis, methodology, confusion matrices for both models, error shift analysis from different categories, and examples illustrating shifts between error categories.
Structure:
- Introduction to Text Representation
- Error Types in Text Sentences
- Methods for Error Correction
- Advanced NLP Models: BART and MarianMT
- Dataset Analysis: C4 Dataset
- Model Training Methodology: Seq2Seq Models
- Error Category Analysis Algorithm
- Results & Discussion: Confusion Matrices for BART & MarianMT
- Error Shift Analysis from Different Categories with Examples
Stats
BART is able to handle spelling errors far better (24.6%) than grammatical errors (8.8%).
MarianMT corrected 20.8% of spelling errors compared to 4.8% of grammatical errors.
BART shifted 9.9% of Cat B sentences to Cat A.
MarianMT shifted 5.4% of Cat B sentences to Cat A.