Abstract
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%).
Abstract (translated)
文本 remains 是一种相关形式的信息表示形式。文本文件可以通过数字原生平台或转换其他媒体文件（如图像和语音）来创建。虽然数字原生文本是通过物理或虚拟键盘获得的，但使用 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引入了错误到捕获到的文本中。本项目旨在分析文本文件中发生的不同类型的错误。该工作采用两个先进的基于深度神经网络的语言模型，即 BART 和 MarianMT，来纠正文本中的异常。使用这些模型的可转移学习来微调其纠正错误的能力。对这两种模型在处理定义的错误类别的效果进行了比较研究。观察到，虽然两种模型都可以将错误的句子降低 20% 以上，但 BART 在处理拼写错误方面远比语法错误（8.8%）要好（24.6%）。
URL
https://arxiv.org/abs/2403.16655
