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IntroductionWith the development of globalization and the increasing importance of English in international communication, effectively improving English writing skills has become a key focus in language learning. Traditional methods for English writing guidance and error correction have predominantly relied on rule-based approaches or statistical models, such as conventional language models and basic machine learning algorithms. While these methods can aid learners in improving their writing quality to some extent, they often suffer from limitations such as inflexibility, insufficient contextual understanding, and an inability to handle multimodal information. These shortcomings restrict their effectiveness in more complex linguistic environments.MethodsTo address these challenges, this study introduces ETG-ALtrans, a multimodal robot-assisted English writing guidance and error correction technology based on an improved ALBEF model and VGG19 architecture, enhanced by reinforcement learning. The approach leverages VGG19 to extract visual features and integrates them with the ALBEF model, achieving precise alignment and fusion of images and text. This enhances the model's ability to comprehend context. Furthermore, by incorporating reinforcement learning, the model can adaptively refine its correction strategies, thereby optimizing the effectiveness of writing guidance.Results and discussionExperimental results demonstrate that the proposed ETG-ALtrans method significantly improves the accuracy of English writing error correction and the intelligence level of writing guidance in multimodal data scenarios. Compared to traditional methods, this approach not only enhances the precision of writing suggestions but also better caters to the personalized needs of learners, thereby effectively improving their writing skills. This research is of significant importance in the field of language learning technology and offers new perspectives and methodologies for the development of future English writing assistance tools. |
