用深度学习将罗马字母拼写的马拉雅拉姆语转为原生文字
Romanized to Native Malayalam Script Transliteration Using an Encoder-Decoder Framework
- 基于注意力机制的双向LSTM模型，实现罗马字母到马拉雅拉姆文的逆向转写
- 在标准测试集上字符错误率仅7.4%，在特殊输入下达22.7%
- 适合处理印度语言文本转换，尤其对输入不规范场景有实用价值
本文提出一种反向转写模型，利用基于注意力机制的双向长短期记忆网络（Bi-LSTM）框架，将罗马化马拉雅拉姆语转换为原生文字。模型训练使用了从公开印地语转写数据集Dakshina和Aksharantar整合而来的430万组转写对。在IndoNLP-2025-Shared-Task提供的两个测试集上进行评估：测试集1（通用输入模式）获得7.4%的字符错误率（CER），测试集2（非标准输入模式，多数元音符号缺失）则达到22.7%的CER。
原文摘要 · Abstract (English)
In this work, we present the development of a reverse transliteration model to convert romanized Malayalam to native script using an encoder-decoder framework built with attention-based bidirectional Long Short Term Memory (Bi-LSTM) architecture. To train the model, we have used curated and combined collection of 4.3 million transliteration pairs derived from publicly available Indic language translitertion datasets, Dakshina and Aksharantar. We evaluated the model on two different test dataset provided by IndoNLP-2025-Shared-Task that contain, (1) General typing patterns and (2) Adhoc typing patterns, respectively. On the Test Set-1, we obtained a character error rate (CER) of 7.4%. However upon Test Set-2, with adhoc typing patterns, where most vowel indicators are missing, our model gave a CER of 22.7%.
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