  Rapidly developed neural models have achieved competitive performance in
Chinese word segmentation (CWS) as their traditional counterparts. However,
most of methods encounter the computational inefficiency especially for long
sentences because of the increasing model complexity and slower decoders. This
paper presents a simple neural segmenter which directly labels the gap
existence between adjacent characters to alleviate the existing drawback. Our
segmenter is fully end-to-end and capable of performing segmentation very fast.
We also show a performance difference with different tag sets. The experiments
show that our segmenter can provide comparable performance with
state-of-the-art.
