语法与拼写错误纠正：使用 BART 和 MarianMT 探究基于 Transformer 的语言模型的响应能力
Grammatical vs Spelling Error Correction: An Investigation into the Responsiveness of Transformer-based Language Models using BART and MarianMT
AI总结：
本项目使用 BART 和 MarianMT 两种深度学习模型对文本中的拼写和语法错误进行纠正，通过迁移学习微调模型，发现两者均能将错误句子减少20%以上，且 BART 处理拼写错误的效果显著优于语法错误。
摘要：
文本继续作为信息表示的相关形式。文本文档要么在数字原生平台上创建，要么通过转换图像和语音等其他媒体文件来创建。虽然数字原生文本总是通过物理或虚拟键盘获取，但利用 OCR 和语音识别等技术将图像和语音信号转换为文本内容。所有这些文本生成机制也会在捕获的文本中引入错误。本项目旨在分析文本文档中出现的不同类型的错误。本工作采用两种先进的基于深度神经网络的语言模型，即 BART 和 MarianMT，来纠正文本中存在的异常。使用可用数据集对这些模型进行迁移学习，以微调其纠错能力。进行了一项比较研究，以调查这些模型在处理每个定义的错误类别时的有效性。观察到，虽然两个模型都能将错误句子减少 20% 以上，但 BART 处理拼写错误的能力（24.6%）远好于语法错误（8.8%）。
英文摘要：
Text continues to remain a relevant form of representation for information. Text documents are created either in digital native platforms or through the conversion of other media files such as images and speech. While the digital native text is invariably obtained through physical or virtual keyboards, technologies such as OCR and speech recognition are utilized to transform the images and speech signals into text content. All these variety of mechanisms of text generation also introduce errors into the captured text. This project aims at analyzing different kinds of error that occurs in text documents. The work employs two of the advanced deep neural network-based language models, namely, BART and MarianMT, to rectify the anomalies present in the text. Transfer learning of these models with available dataset is performed to finetune their capacity for error correction. A comparative study is conducted to investigate the effectiveness of these models in handling each of the defined error categories. It is observed that while both models can bring down the erroneous sentences by 20+%, BART can handle spelling errors far better (24.6%) than grammatical errors (8.8%).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
