  This paper presents a neural architecture for Vietnamese sequence labeling
tasks including part-of-speech (POS) tagging and named entity recognition
(NER). We applied the model described in \cite{lample-EtAl:2016:N16-1} that is
a combination of bidirectional Long-Short Term Memory and Conditional Random
Fields, which rely on two sources of information about words: character-based
word representations learned from the supervised corpus and pre-trained word
embeddings learned from other unannotated corpora. Experiments on benchmark
datasets show that this work achieves state-of-the-art performances on both
tasks - 93.52\% accuracy for POS tagging and 94.88\% F1 for NER. Our sourcecode
is available at here.
