Abstract
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 - this https URL
Abstract (translated)
由于便利性和技术知识的缺乏，音译（即用罗马字母拼写本地脚本而非使用本土化工具）在如僧伽罗语这样的资源匮乏语言中非常普遍，尽管这些语言有自己独特的书写系统。在这项研究中，我们的重点是罗马化的僧伽罗语文本转写。我们提出了两种方法来解决这个问题：一种是基于规则的方法作为基准，然后将其与另一种方法进行比较，在这种方法中，我们将音译问题视为类似成熟的神经机器翻译（NMT）任务的序列到序列任务。对于后者，我们提出了一种基于Transformer的编码-解码解决方案。我们发现，相比于基于规则的方法，基于Transformer的方法能够抓住罗马化脚本中的许多临时模式。与这篇论文相关的代码库可在GitHub上获取 - 这个链接：[请在此处插入实际URL]
URL
https://arxiv.org/abs/2501.00529
