对比规则与神经网络方法，提升僧伽罗语罗马化转写准确率。
Sinhala Transliteration: A Comparative Analysis Between Rule-based and Seq2Seq Approaches
- 用规则方法和Transformer序列模型两种思路解决转写问题。
- 神经模型在捕捉罗马字母模式上表现更优，准确率更高。
- 适合低资源语言处理、自然语言生成研究者参考。
由于使用便利性和技术素养不足，低资源语言如僧伽罗语常采用罗马化转写（即用拉丁字母替代本地文字），而非依赖本地化工具。本文聚焦于僧伽罗语的罗马化转写任务，提出两种方法：基线为规则驱动方法，另一方法将转写视为序列到序列任务，采用基于Transformer的编码器-解码器结构。实验表明，相比规则方法，神经模型能更好捕捉罗马化文本中的复杂模式。相关代码已公开于GitHub：https://github.com/kasunw22/Sinhala-Transliterator/
原文摘要 · Abstract (English)
Due to reasons of convenience and lack of tech literacy, transliteration (i.e., Romanizing native scripts instead of using localization tools) is eminently prevalent in the context of low-resource languages such as Sinhala, which have their own writing script. In this study, our focus is on Romanized Sinhala transliteration. We propose two methods to address this problem: Our baseline is a rule-based method, which is then compared against our second method where we approach the transliteration problem as a sequence-to-sequence task akin to the established Neural Machine Translation (NMT) task. For the latter, we propose a Transformer-based Encode-Decoder solution. We witnessed that the Transformer-based method could grab many ad-hoc patterns within the Romanized scripts compared to the rule-based method. The code base associated with this paper is available on GitHub - https://github.com/kasunw22/Sinhala-Transliterator/
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