  This paper presents an empirical study of two widely-used sequence prediction
models, Conditional Random Fields (CRFs) and Long Short-Term Memory Networks
(LSTMs), on two fundamental tasks for Vietnamese text processing, including
part-of-speech tagging and named entity recognition. We show that a strong
lower bound for labeling accuracy can be obtained by relying only on simple
word-based features with minimal hand-crafted feature engineering, of 90.65\%
and 86.03\% performance scores on the standard test sets for the two tasks
respectively. In particular, we demonstrate empirically the surprising
efficiency of word embeddings in both of the two tasks, with both of the two
models. We point out that the state-of-the-art LSTMs model does not always
outperform significantly the traditional CRFs model, especially on
moderate-sized data sets. Finally, we give some suggestions and discussions for
efficient use of sequence labeling models in practical applications.
