为不丹语杜仲克哈设计高效输入预测系统，减少打字次数。
Dzongkha Next Word Prediction System
- 基于N-gram与文本预处理构建语言模型，测试LSTM/Bi-LSTM/GRU三类结构。
- GRU模型在10万句、超过130万词数据上达74.03%准确率，避免过拟合。
- 适合需要提升杜仲克哈输入效率的本地化应用与数字文档建设者。
杜仲克哈是不丹的国语，广泛用于官方文件、经文及文献，但其书写复杂，每音节需多键输入，且缺乏高效输入工具。本研究旨在通过单词预测系统简化打字流程。数据来自DCDD，共包含10万条句子、1331282个词和28344个唯一词。经过去除非字母字符、分词、生成N-gram序列及填充等预处理后，选用LSTM、Bi-LSTM和GRU三种模型进行训练，并对超参数进行微调。结果表明，轻量级的GRU模型表现最佳，准确率达74.03%，且有效缓解了过拟合问题。
原文摘要 · Abstract (English)
Dzongkha, being the national language of Bhutan, is a common and widely spoken language in the country. Official documents, scriptures and other literature products are written in Dzongkha in order to retain the cultural value. However, documenting Dzongkha writing is a challenging and time-consuming process, largely due to the complexity of the script, the need for multiple keystrokes per syllable, and the limited availability of efficient typing tools. An immediate system that can predict and display a list of probable words for Dzongkha is the solution for this problem. The project is mainly aimed to make Dzongkha typing as convenient as possible by reducing the number of keystrokes. Our dataset is acquired from DCDD and has a total of 100000 sentences, 1331282 words and 28344 unique words. The data preprocessing was done by removing all the alphanumeric characters, tokenization, generating N-gram sequences and padding. Three models selected for training are LSTM, Bi-LSTM and GRU. The training process included fine-tuning of the model's hyperparameters. GRU being lightweight and able to handle larger datasets performed best with 74.03% accuracy and also solved the problem of overfitting.
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