Machine Transliteration provides the ability to transliterate a basic
language into different languages in a computational way. Transliteration is an
important technical process that has caught the attention most recently. The
Sinhala transliteration has many constraints because of the insufficiency of
resources in the Sinhala language. Due to these limitations, Sinhala
Transliteration is highly complex and time-consuming. Therefore, the majority
of the Sri Lankans uses non-formal texting language named 'Singlish' to make
that process simple. This study has focused on the transliteration of the
Singlish language at the word level by reducing the complication in the
transliteration. A new approach of coding system has invented with the
rule-based approach that can map the matching Sinhala words even without the
vowels. Various typing patterns were collected by different communities for
this. The collected data have analyzed with every Sinhala character and unique
Singlish patterns related to them were generated. The system has introduced a
newly initiated numeric coding system to use with the Singlish letters by
matching with the recognized typing patterns. For the mapping process, fuzzy
logic-based implementation has used. A codified dictionary has also implemented
including unique numeric values. In this system, Each Romanized English letter
was assigned with a unique numeric code that can construct a unique pattern for
each word. The system can identify the most relevant Sinhala word that matches
with the pattern of the Singlish word or it gives the most related word
suggestions. For example, the word 'kiyanna,kianna, kynna, kynn, kiynna' have
mapped with the accurate Sinhala word "kiyanna". These results revealed that
the 'Swa Bhasha' transliteration system has the ability to enhance the Sinhala
users' experience while conducting the texting in Singlish to Sinhala.

该研究聚焦于通过减少转写中的复杂性，从词级别对 Singlish 语言进行转译，应用基于规则的编码系统，利用模糊逻辑实现匹配，并提供相关词建议，结果表明这种 Swa Bhasha 转译系统能够提升使用 Singlish 向 Sinhala 转写的用户体验。

基于信息的 Singlish 到 Sinhala 音译

Swa Bhasha: Message-Based Singlish to Sinhala Transliteration

The widespread of offensive content online, such as hate speech and
cyber-bullying, is a global phenomenon. This has sparked interest in the
artificial intelligence (AI) and natural language processing (NLP) communities,
motivating the development of various systems trained to detect potentially
harmful content automatically. These systems require annotated datasets to
train the machine learning (ML) models. However, with a few notable exceptions,
most datasets on this topic have dealt with English and a few other
high-resource languages. As a result, the research in offensive language
identification has been limited to these languages. This paper addresses this
gap by tackling offensive language identification in Sinhala, a low-resource
Indo-Aryan language spoken by over 17 million people in Sri Lanka. We introduce
the Sinhala Offensive Language Dataset (SOLD) and present multiple experiments
on this dataset. SOLD is a manually annotated dataset containing 10,000 posts
from Twitter annotated as offensive and not offensive at both sentence-level
and token-level, improving the explainability of the ML models. SOLD is the
first large publicly available offensive language dataset compiled for Sinhala.
We also introduce SemiSOLD, a larger dataset containing more than 145,000
Sinhala tweets, annotated following a semi-supervised approach.

本研究针对斯里兰卡使用人数超过 1700 万的 Sinhala 语，开发了一个手工注释的数据集 SOLD，和一个更大的半监督数据集 SemiSOLD，用以改进机器学习模型鉴别 Sinhala 语的具有攻击性的语言的能力。