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 https://github.com/kasunw22/sinhala-para-dict.

翻译：平行数据集对于执行和评估任何多语言任务至关重要。然而，当所涉及的语言对之一为低资源语言时，由于人工标注的匮乏，现有的自上而下平行数据（如语料库）在数量和质量上均显不足。因此，对于低资源语言而言，采用自下而上的方法更为可行，即首先开发细粒度较高的配对数据集（如词典数据集）。这些数据集随后可用于中等任务，如监督式多语言词嵌入对齐。这些对齐结果进而可指导更高级任务，如用于机器翻译的句子或段落级文本语料库对齐。尽管比生成和对齐低资源语言的大规模语料库更为可行，但出于相同原因（大型研究实体对此类语言关注不足），即使这些细粒度的数据集在一些低资源语言中仍然匮乏。我们观察到，低资源语言僧伽罗语目前缺乏免费开放的词典数据集。因此，在本工作中，我们引入了三个平行英语-僧伽罗语单词词典（En-Si-dict-large、En-Si-dict-filtered、En-Si-dict-FastText），以辅助涉及英语和僧伽罗语的多语言自然语言处理任务。本文阐述了数据集创建流程以及为验证数据集质量所进行的测试实验结果。数据集及相关脚本可在 https://github.com/kasunw22/sinhala-para-dict 获取。