The introduction of large language models (LLMs) has advanced natural
language processing (NLP), but their effectiveness is largely dependent on
pre-training resources. This is especially evident in low-resource languages,
such as Sinhala, which face two primary challenges: the lack of substantial
training data and limited benchmarking datasets. In response, this study
introduces NSINA, a comprehensive news corpus of over 500,000 articles from
popular Sinhala news websites, along with three NLP tasks: news media
identification, news category prediction, and news headline generation. The
release of NSINA aims to provide a solution to challenges in adapting LLMs to
Sinhala, offering valuable resources and benchmarks for improving NLP in the
Sinhala language. NSINA is the largest news corpus for Sinhala, available up to
date.

本研究介绍了 NSINA，这是一个由 500,000 篇流行信拉语新闻网站上的文章构成的全面新闻语料库，并提出了三个自然语言处理任务：新闻媒体识别、新闻类别预测和新闻标题生成，旨在解决适用 LLMs 到信拉语的挑战，并为改进信拉语自然语言处理提供宝贵的资源和基准。NSINA 是迄今为止最大的信拉语新闻语料库。

NSINA：斯巴达的新闻语料库

NSINA: A News Corpus for Sinhala

In the process of numerically modeling natural languages, developing language
embeddings is a vital step. However, it is challenging to develop functional
embeddings for resource-poor languages such as Sinhala, for which sufficiently
large corpora, effective language parsers, and any other required resources are
difficult to find. In such conditions, the exploitation of existing models to
come up with an efficacious embedding methodology to numerically represent text
could be quite fruitful. This paper explores the effectivity of several
one-tiered and two-tiered embedding architectures in representing Sinhala text
in the sentiment analysis domain. With our findings, the two-tiered embedding
architecture where the lower-tier consists of a word embedding and the
upper-tier consists of a sentence embedding has been proven to perform better
than one-tier word embeddings, by achieving a maximum F1 score of 88.04% in
contrast to the 83.76% achieved by word embedding models. Furthermore,
embeddings in the hyperbolic space are also developed and compared with
Euclidean embeddings in terms of performance. A sentiment data set consisting
of Facebook posts and associated reactions have been used for this research. To
effectively compare the performance of different embedding systems, the same
deep neural network structure has been trained on sentiment data with each of
the embedding systems used to encode the text associated.

本文研究多种单层和双层嵌入结构在 Sinhala 情感分析中的有效性，结果表明，基于单层词嵌入的模型的得分为 83.76％，而基于上下层的嵌入结构的最大 F1 得分为 88.04％，并比较欧几里得空间和双曲空间中嵌入的性能。

Sinhala 句子嵌入：低资源语言的两层结构

Sinhala Sentence Embedding: A Two-Tiered Structure for Low-Resource Languages

Sinhala is the native language of the Sinhalese people who make up the
largest ethnic group of Sri Lanka. The language belongs to the globe-spanning
language tree, Indo-European. However, due to poverty in both linguistic and
economic capital, Sinhala, in the perspective of Natural Language Processing
tools and research, remains a resource-poor language which has neither the
economic drive its cousin English has nor the sheer push of the law of numbers
a language such as Chinese has. A number of research groups from Sri Lanka have
noticed this dearth and the resultant dire need for proper tools and research
for Sinhala natural language processing. However, due to various reasons, these
attempts seem to lack coordination and awareness of each other. The objective
of this paper is to fill that gap of a comprehensive literature survey of the
publicly available Sinhala natural language tools and research so that the
researchers working in this field can better utilize contributions of their
peers. As such, we shall be uploading this paper to arXiv and perpetually
update it periodically to reflect the advances made in the field.

本文对公开可用的信杭拉自然语言处理工具和研究进行了全面的文献综述，以帮助该领域的研究人员更好地利用同行的贡献。