Design of intelligent proofreading system for English translation based on CNN and BERT

TL;DR

This paper introduces a hybrid CNN-BERT model for English translation proofreading that effectively detects and corrects errors, significantly improving accuracy and outperforming existing methods.

Contribution

A novel end-to-end hybrid CNN and BERT-based system for robust translation proofreading with integrated detection and correction modules.

Findings

Achieved 90% accuracy in error detection.

Attained 89.37% F1 score in proofreading.

Outperformed recent techniques by over 10%.

Abstract

Since automatic translations can contain errors that require substantial human post-editing, machine translation proofreading is essential for improving quality. This paper proposes a novel hybrid approach for robust proofreading that combines convolutional neural networks (CNN) with Bidirectional Encoder Representations from Transformers (BERT). In order to extract semantic information from phrases and expressions, CNN uses a variety of convolution kernel filters to capture local n-gram patterns. In the meanwhile, BERT creates context-rich representations of whole sequences by utilizing stacked bidirectional transformer encoders. Using BERT's attention processes, the integrated error detection component relates tokens to spot translation irregularities including word order problems and omissions. The correction module then uses parallel English-German alignment and GRU decoder models in conjunction with translation memory to propose logical modifications that maintain original meaning. A unified end-to-end training process optimized for post-editing performance is applied to the whole pipeline. The multi-domain collection of WMT and the conversational dialogues of Open-Subtitles are two of the English-German parallel corpora used to train the model. Multiple loss functions supervise detection and correction capabilities. Experiments attain a 90% accuracy, 89.37% F1, and 16.24% MSE, exceeding recent proofreading techniques by over 10% overall. Comparative benchmarking demonstrates state-of-the-art performance in identifying and coherently rectifying mistranslations and omissions.