标题： 僧伽罗语转写：基于规则和Seq2Seq方法的比较分析 显示英文标题

标题： Sinhala Transliteration: A Comparative Analysis Between Rule-based and Seq2Seq Approaches

摘要： 由于便利性和技术素养不足，音译（即用拉丁字母转写本土文字而不是使用本地化工具）在低资源语言如僧伽罗语的背景下非常普遍，这些语言有自己的书写文字。 在本研究中，我们的重点是拉丁化的僧伽罗语音译。 我们提出了两种方法来解决这个问题：我们的基线方法是一种基于规则的方法，然后将其与我们的第二种方法进行比较，我们在第二种方法中将音译问题视为类似于已建立的神经机器翻译（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的编码器-解码器解决方案。 我们观察到，与基于规则的方法相比，基于Transformer的方法能够捕捉许多临时的模式在拉丁化脚本中。 与本文相关的代码库可在GitHub上获取 - https://github.com/kasunw22/Sinhala-Transliterator/ 显示英文摘要

摘要： 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/