  Word segmentation is a fundamental pre-processing step for Thai Natural
Language Processing. The current off-the-shelf solutions are not benchmarked
consistently, so it is difficult to compare their trade-offs. We conducted a
speed and accuracy comparison of the popular systems on three different domains
and found that the state-of-the-art deep learning system is slow and moreover
does not use sub-word structures to guide the model. Here, we propose a fast
and accurate neural Thai Word Segmenter that uses dilated CNN filters to
capture the environment of each character and uses syllable embeddings as
features. Our system runs at least 5.6x faster and outperforms the previous
state-of-the-art system on some domains. In addition, we develop the first
ML-based Thai orthographical syllable segmenter, which yields syllable
embeddings to be used as features by the word segmenter.
