基于类型和词元语言建模的历史德语文本规范化
Historical German Text Normalization Using Type- and Token-Based Language Modeling
AI总结：
针对历史德语拼写变体问题，提出结合编码器-解码器与因果语言模型的Transformer规范化系统，在1700-1900年文学文本上达到最先进准确性，但泛化与数据仍具挑战。
摘要：
历史拼写变体对历史数字化文本的全文搜索或自然语言处理构成了挑战。为缩小历史正字法与当代拼写之间的差距，通常需要对历史原始材料进行自动正字法规范化。本报告提出了一种针对约1700-1900年德语文学文本的规范化系统，该系统在平行语料库上训练。所提出的系统采用基于Transformer语言模型的机器学习方法，结合编码器-解码器模型对单个词类型进行规范化，以及一个预训练的因果语言模型在上下文中调整这些规范化结果。广泛评估表明，所提出的系统提供了最先进的准确性，与一个更大的完全端到端基于句子的规范化系统（微调预训练Transformer大语言模型）相当。然而，由于模型泛化困难以及缺乏大量高质量平行数据，历史文本的规范化仍然是一个挑战。
英文摘要：
Historic variations of spelling poses a challenge for full-text search or natural language processing on historical digitized texts. To minimize the gap between the historic orthography and contemporary spelling, usually an automatic orthographic normalization of the historical source material is pursued. This report proposes a normalization system for German literary texts from c. 1700-1900, trained on a parallel corpus. The proposed system makes use of a machine learning approach using Transformer language models, combining an encoder-decoder model to normalize individual word types, and a pre-trained causal language model to adjust these normalizations within their context. An extensive evaluation shows that the proposed system provides state-of-the-art accuracy, comparable with a much larger fully end-to-end sentence-based normalization system, fine-tuning a pre-trained Transformer large language model. However, the normalization of historical text remains a challenge due to difficulties for models to generalize, and the lack of extensive high-quality parallel data.
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。
