  A wide variety of neural-network architectures have been proposed for the
task of Chinese word segmentation.
  Surprisingly, we find that a bidirectional LSTM model, when combined with
standard deep learning techniques and best practices, can achieve better
accuracy on many of the popular datasets as compared to models based on more
complex neural-network architectures.
  Furthermore, our error analysis shows that out-of-vocabulary words remain
challenging for neural-network models, and many of the remaining errors are
unlikely to be fixed through architecture changes.
  Instead, more effort should be made on exploring resources for further
improvement.
