Context-Aware Adversarial Graph-Based Learning for Multilingual Grammatical Error Correction

Correcting grammatical errors in various language contexts is a crucial and challenging task in the field of natural language processing, commonly referred to as Multilingual Grammatical Error Correction. This paper elaborates the Adversarial Temporal Graph Convolution Model (AT-GCM), which combines the capabilities of MT-5, adversarial learning, and temporal graph convolutional neural network (t-GCN) to achieve accurate progress in multilingual grammatical error correction. The inherent capability of MT-5 to process multiple languages simultaneously serves as a powerful embedding generator for the purpose of multilingual error correction. The t-GCN is employed for the purpose of navigating the temporal context and interdependencies present within words. The assumption that modeling the dynamic interactions among words within the context of temporal relationships improves precision, particularly in languages with complex sentence structures, is supported by research. The utilization of adversarial learning techniques can enhance the generalization capabilities of the model across various language pairings, effectively addressing the challenges associated with low-resource languages. A comprehensive analysis is carried out on a diverse, multilingual dataset comprising various languages, viz. English, Russian, German, Czech, Arabic, and Romanian. The experimental results present significant improvements in grammatical error correction performance compared to state-of-the-art models. Our approach effectively resolves grammatical errors in various linguistic contexts by utilizing a combination of MT-5, adversarial learning, and t-GCN.