用多层LSTM与注意力机制自动补全古汉语标点，提升理解准确率。
Predicting Punctuation in Ancient Chinese Texts: A Multi-Layered LSTM and Attention-Based Approach
- 采用双向多层LSTM结合多头注意力模型处理文本序列
- 在古汉语标点预测任务中显著优于普通RNN模型
- 适合古籍数字化、语言学研究者使用
直到20世纪，汉语才开始使用标点符号。许多古代中文文献长达数千行，几乎无任何标点或分隔标记。缺乏标点使读者难以判断句中停顿与语义断点，导致不同解读。本文提出一种基于双向多层LSTM与多头注意力机制的标点预测方法，改进了Oh等（2017）的工作。实验表明，该方法在古汉语文本上的标点预测性能显著优于未使用这些组件的RNN模型。
原文摘要 · Abstract (English)
It was only until the 20th century when the Chinese language began using punctuation. In fact, many ancient Chinese texts contain thousands of lines with no distinct punctuation marks or delimiters in sight. The lack of punctuation in such texts makes it difficult for humans to identify when there pauses or breaks between particular phrases and understand the semantic meaning of the written text (Mogahed, 2012). As a result, unless one was educated in the ancient time period, many readers of ancient Chinese would have significantly different interpretations of the texts. We propose an approach to predict the location (and type) of punctuation in ancient Chinese texts that extends the work of Oh et al (2017) by leveraging a bidirectional multi-layered LSTM with a multi-head attention mechanism as inspired by Luong et al.'s (2015) discussion of attention-based architectures. We find that the use of multi-layered LSTMs and multi-head attention significantly outperforms RNNs that don't incorporate such components when evaluating ancient Chinese texts.
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