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

Singlish can be interesting to the ACL community both linguistically as a
major creole based on English, and computationally for information extraction
and sentiment analysis of regional social media. We investigate dependency
parsing of Singlish by constructing a dependency treebank under the Universal
Dependencies scheme, and then training a neural network model by integrating
English syntactic knowledge into a state-of-the-art parser trained on the
Singlish treebank. Results show that English knowledge can lead to 25% relative
error reduction, resulting in a parser of 84.47% accuracies. To the best of our
knowledge, we are the first to use neural stacking to improve cross-lingual
dependency parsing on low-resource languages. We make both our annotation and
parser available for further research.

本研究使用神经叠加的方法，在 Universal Dependencies 方案下构建了 Singlish 依赖树库，并将英语语法知识整合到最新的 Singlish 依赖分析器中，结果显示英语知识可以减少 25% 的错误率，使分析器的准确度为 84.47%。我们是首次在低资源语言上使用神经叠加来改善跨语言依赖解析。我们会将我们的注释和分析器提供给进一步的研究。