Parallel datasets are vital for performing and evaluating any kind of
multilingual task. However, in the cases where one of the considered language
pairs is a low-resource language, the existing top-down parallel data such as
corpora are lacking in both tally and quality due to the dearth of human
annotation. Therefore, for low-resource languages, it is more feasible to move
in the bottom-up direction where finer granular pairs such as dictionary
datasets are developed first. They may then be used for mid-level tasks such as
supervised multilingual word embedding alignment. These in turn can later guide
higher-level tasks in the order of aligning sentence or paragraph text corpora
used for Machine Translation (MT). Even though more approachable than
generating and aligning a massive corpus for a low-resource language, for the
same reason of apathy from larger research entities, even these finer granular
data sets are lacking for some low-resource languages. We have observed that
there is no free and open dictionary data set for the low-resource language,
Sinhala. Thus, in this work, we introduce three parallel English-Sinhala word
dictionaries (En-Si-dict-large, En-Si-dict-filtered, En-Si-dict-FastText) which
help in multilingual Natural Language Processing (NLP) tasks related to English
and Sinhala languages. In this paper, we explain the dataset creation pipeline
as well as the experimental results of the tests we have carried out to verify
the quality of the data sets. The data sets and the related scripts are
available at this https URL

为了解决低资源语言缺乏人工标注的问题，本研究提出了三个用于英语和僧伽罗语自然语言处理任务的平行英 - 僧伽罗词典数据集，并介绍了数据集创建流程和验证数据集质量的实验结果。