  Thai is a low-resource language, so it is often the case that data is not
available in sufficient quantities to train an Neural Machine Translation (NMT)
model which perform to a high level of quality. In addition, the Thai script
does not use white spaces to delimit the boundaries between words, which adds
more complexity when building sequence to sequence models. In this work, we
explore how to augment a set of English--Thai parallel data by replicating
sentence-pairs with different word segmentation methods on Thai, as training
data for NMT model training. Using different merge operations of Byte Pair
Encoding, different segmentations of Thai sentences can be obtained. The
experiments show that combining these datasets, performance is improved for NMT
models trained with a dataset that has been split using a supervised splitting
tool.
