  The performance of the Chinese Word Segmentation (CWS) systems has gradually
reached a plateau with the rapid development of deep neural networks,
especially the successful use of large pre-trained models. In this paper, we
take stock of what we have achieved and rethink what's left in the CWS task.
Methodologically, we propose a fine-grained evaluation for existing CWS
systems, which not only allows us to diagnose the strengths and weaknesses of
existing models (under the in-dataset setting), but enables us to quantify the
discrepancy between different criterion and alleviate the negative transfer
problem when doing multi-criteria learning. Strategically, despite not aiming
to propose a novel model in this paper, our comprehensive experiments on eight
models and seven datasets, as well as thorough analysis, could search for some
promising direction for future research. We make all codes publicly available
and release an interface that can quickly evaluate and diagnose user's models:
https://github.com/neulab/InterpretEval.
