Introducing a fusion model of language content attention mechanisms and structural embeddings to achieve automatic scoring of English writing

Overview The Fusion Language Content Attention Model (FLCAM) is an advanced framework designed to evaluate English writing quality through a unified integration of semantic content analysis and structural embeddings. The model incorporates a multimodal encoder, graphical propagation mechanisms, and an adaptive fusion strategy to deliver highly accurate, consistent, and interpretable scoring outcomes. This repository provides a complete implementation of the framework, along with dataset guidelines, experimental benchmarks, and architectural illustrations. The goal of FLCAM is to improve automated writing assessment by combining linguistic understanding with structural organization analysis. It is particularly effective for tasks that require deep comprehension of vocabulary richness, coherence, syntactic complexity, and document structure. Key Features Language Content Attention The model captures semantic depth by applying a token level attention mechanism that highlights linguistically significant components of a text. It identifies relevance between words and contextual structures, enabling a more precise estimation of content quality. Structural Embedding Integration FLCAM introduces a structural embedding matrix derived from syntactic features such as part of speech tags, dependency relations, sentence level organization, and paragraph level coherence. This structural layer provides insight into the logical flow and grammatical consistency within a document. Multimodal Encoder A multimodal encoder merges contextual embeddings from a pretrained language model with structural vectors. The encoder supports hierarchical analysis, allowing the model to observe relationships across words, sentences, and document sections. Graphical Propagation A graphical propagation layer models internal relationships among tokens by assigning dynamic attention coefficients that represent semantic and structural relevance. This mechanism captures complex linguistic dependencies more effectively than conventional sequence based encoders. Innovative Fusion Strategy The fusion strategy balances content attention and structural information through a joint optimization objective. A feedback loop iteratively adjusts parameters to adapt to diverse writing styles and proficiency levels. This results in improved robustness, generalization, and scoring stability. Architecture Summary FLCAM is composed of the following components: A pretrained language model encoder that generates contextual embeddings A structural embedding generator based on linguistic annotations A multimodal fusion encoder for combining semantic and structural information A graphical propagation module for modeling long range dependencies A global pooling mechanism for document level representation A fully connected scoring head that predicts final writing quality scores Visual diagrams demonstrating the architecture and fusion strategy are included in the documentation directory. Experimental Results Extensive experiments conducted on multiple writing datasets show that FLCAM achieves superior performance when compared with deep learning baselines such as ResNet, ViT, DenseNet, MobileNet, and combinations of CNN, LSTM, and BiLSTM models. The model demonstrates improvements across accuracy, precision, recall, F1 score, and AUC metrics. Ablation studies further confirm the importance of each architectural component, illustrating the contributions of the multimodal encoder, graphical propagation layer, and adaptive feedback strategy. Dataset Information This project supports multiple public datasets commonly used in automated writing evaluation research. Examples include datasets focused on writing quality analysis, structural embedding representation, language attention scoring, and automated essay evaluation. Guidelines for dataset preparation and expected annotation formats are provided in the dataset documentation. Installation The project requires Python along with common deep learning libraries such as PyTorch, NumPy, and other standard NLP tools.A detailed installation guide and environment configuration file are provided in the setup directory. Future Work Planned improvements include multilingual writing evaluation, interactive feedback generation for learners, broader dataset integration, and more explainable scoring visualizations. Additional extensions may include adaptive writing assistance and cross domain evaluation tasks. License This project is released under the MIT License.Please review the license file for additional information.