Wang Ni
School of Humanities and International Education, Xi'an Peihua University, Xi'an, Shaanxi, China.
Front Neurorobot. 2024 Nov 20;18:1483131. doi: 10.3389/fnbot.2024.1483131. eCollection 2024.
With 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.
To 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.
Experimental 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.
随着全球化的发展以及英语在国际交流中日益重要，有效提高英语写作技能已成为语言学习的关键重点。传统的英语写作指导和纠错方法主要依赖基于规则的方法或统计模型，如传统语言模型和基本机器学习算法。虽然这些方法在一定程度上可以帮助学习者提高写作质量，但它们往往存在灵活性不足、上下文理解不够以及无法处理多模态信息等局限性。这些缺点限制了它们在更复杂语言环境中的有效性。
为应对这些挑战，本研究引入了ETG-ALtrans，这是一种基于改进的ALBEF模型和VGG19架构、通过强化学习增强的多模态机器人辅助英语写作指导和纠错技术。该方法利用VGG19提取视觉特征，并将其与ALBEF模型集成，实现图像和文本的精确对齐与融合。这增强了模型理解上下文的能力。此外，通过纳入强化学习，模型可以自适应地优化其纠错策略，从而提高写作指导的有效性。
实验结果表明，所提出的ETG-ALtrans方法在多模态数据场景中显著提高了英语写作纠错的准确性和写作指导的智能水平。与传统方法相比，该方法不仅提高了写作建议的精度，还更好地满足了学习者的个性化需求，从而有效地提高了他们的写作技能。本研究在语言学习技术领域具有重要意义，并为未来英语写作辅助工具的开发提供了新的视角和方法。
