  Word segmentation and part-of-speech tagging are two critical preliminary
steps for downstream tasks in Vietnamese natural language processing. In
reality, people tend to consider also the phrase boundary when performing word
segmentation and part of speech tagging rather than solely process word by word
from left to right. In this paper, we implement this idea to improve word
segmentation and part of speech tagging the Vietnamese language by employing a
simplified constituency parser. Our neural model for joint word segmentation
and part-of-speech tagging has the architecture of the syllable-based CRF
constituency parser. To reduce the complexity of parsing, we replace all
constituent labels with a single label indicating for phrases. This model can
be augmented with predicted word boundary and part-of-speech tags by other
tools. Because Vietnamese and Chinese have some similar linguistic phenomena,
we evaluated the proposed model and its augmented versions on three Vietnamese
benchmark datasets and six Chinese benchmark datasets. Our experimental results
show that the proposed model achieves higher performances than previous works
for both languages.
