Multiple Segmentations of Thai Sentences for Neural Machine Translation
This work addresses translation quality issues for Thai, a low-resource language with no word boundaries, but it is incremental as it builds on existing segmentation methods.
The paper tackles the problem of low-resource Neural Machine Translation for Thai by augmenting parallel English-Thai data with multiple word segmentations using Byte Pair Encoding, resulting in improved performance for models trained on supervised split data.
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.