  Khmer text is written from left to right with optional space. Space is not
served as a word boundary but instead, it is used for readability or other
functional purposes. Word segmentation is a prior step for downstream tasks
such as part-of-speech (POS) tagging and thus, the robustness of POS tagging
highly depends on word segmentation. The conventional Khmer POS tagging is a
two-stage process that begins with word segmentation and then actual tagging of
each word, afterward. In this work, a joint word segmentation and POS tagging
approach using a single deep learning model is proposed so that word
segmentation and POS tagging can be performed spontaneously. The proposed model
was trained and tested using the publicly available Khmer POS dataset. The
validation suggested that the performance of the joint model is on par with the
conventional two-stage POS tagging.
