  Out-of-Vocabulary (OOV) is a problem for Neural Machine Translation (NMT).
OOV refers to words with a low occurrence in the training data, or to those
that are absent from the training data. To alleviate this, word or phrase-based
Data Augmentation (DA) techniques have been used. However, existing DA
techniques have addressed only one of these OOV types and limit to considering
either syntactic constraints or semantic constraints. We present a word and
phrase replacement-based DA technique that consider both types of OOV, by
augmenting (1) rare words in the existing parallel corpus, and (2) new words
from a bilingual dictionary. During augmentation, we consider both syntactic
and semantic properties of the words to guarantee fluency in the synthetic
sentences. This technique was experimented with low resource Sinhala-English
language pair. We observe with only semantic constraints in the DA, the results
are comparable with the scores obtained considering syntactic constraints, and
is favourable for low-resourced languages that lacks linguistic tool support.
Additionally, results can be further improved by considering both syntactic and
semantic constraints.
