Out of vocabulary (OOV) is a problem in the context of Machine Translation
(MT) in low-resourced languages. When source and/or target languages are
morphologically rich, it becomes even worse. Bilingual list integration is an
approach to address the OOV problem. This allows more words to be translated
than are in the training data. However, since bilingual lists contain words in
the base form, it will not translate inflected forms for morphologically rich
languages such as Sinhala and Tamil. This paper focuses on data augmentation
techniques where bilingual lexicon terms are expanded based on case-markers
with the objective of generating new words, to be used in Statistical machine
Translation (SMT). This data augmentation technique for dictionary terms shows
improved BLEU scores for Sinhala-English SMT.

本文主要研究针对机器翻译中低资源语种的词汇缺失问题，提出了基于双语词表整合的方法和基于数据增强技术的方法，通过以词干和格标注扩充双语词表词汇以提高 SMT 的准确性，在 Sinhala-English 机器翻译任务中取得了改善。