Before revision:  The joint-model is trained and evaluated in 13 corpora of four tasks, yielding near state-of-the-art (SOTA) performance in the dependency parsing task and SOTA performance in the other three tasks .
After revision:  The joint-model is trained and evaluated in 13 corpora of four tasks, yielding near state-of-the-art (SOTA) performance in the dependency parsing task and SOTA performance in CWS and POS. Besides, fastHan's transferability is also strong, performing much better than popular segmentation tools on a non-training corpus. To better meet the need of practical application, we allow users to use their own labeled data to further fine-tune fastHan .