僧伽罗语音译：基于规则与Seq2Seq方法的对比分析
Sinhala Transliteration: A Comparative Analysis Between Rule-based and Seq2Seq Approaches
AI总结：
本研究针对低资源语言僧伽罗语的罗马化音译问题，提出了基于规则和基于Transformer的Seq2Seq两种方法，对比发现后者能更好地捕捉罗马化文字中的临时模式。
摘要：
出于便利性和缺乏技术素养的原因，在拥有自己书写系统的僧伽罗语等低资源语言环境中，音译（即将本地文字罗马化，而非使用本地化工具）极为普遍。本研究聚焦于罗马化僧伽罗语音译。我们提出两种方法来解决此问题：基线是一种基于规则的方法，并将其与第二种方法进行对比，后者将音译问题视为类似于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的编码器-解码器解决方案。我们发现，与基于规则的方法相比，基于Transformer的方法能够捕捉罗马化文字中的许多临时模式。本文相关代码库已在GitHub上公开。
英文摘要：
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/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
