Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: State-of-the-art Chinese Word Segmentation with Bi-LSTMs   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.

34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph