1. The proposed method is not compared with other CWS models. The baseline model (Bi-LSTM) is proposed in [1] and [2]. However, these model is proposed not for CWS but for POS tagging and NE tagging. The description "In this paper, we employ the state-of-the-art architecture ..." (in Section 2) is misleading.
2. The purpose of experiments in Section 6.4 is unclear. In Sec. 6.4, the purpose is that investigating "datasets in traditional Chinese and simplified Chinese could help each other." However, in the experimental setting, the model is separately trained on simplified Chinese and traditional Chinese, and the shared parameters are fixed after training on simplified Chinese. What is expected to fixed shared parameters?
- General Discussion: The paper should be more interesting if there are more detailed discussion about the datasets that adversarial multi-criteria learning does not boost the performance.
[1] Zhiheng Huang, Wei Xu, and Kai Yu. 2015. Bidirectional lstm-crf models for sequence tagging. arXiv preprint arXiv:1508.01991.
[2] Xuezhe Ma and Eduard Hovy. 2016. End-to-end sequence labeling via bi-directional lstm-cnns-crf. arXiv preprint arXiv:1603.01354 .
1. The proposed method is not compared with other CWS models. The baseline model (Bi-LSTM) is proposed in [1] and [2]. However, these model is proposed not for CWS but for POS tagging and NE tagging. The description "In this paper, we employ the state-of-the-art architecture ..." (in Section 2) is misleading.