Particularly, we propose a Siamese Labeled Topic Model (SLTM) to exploit the information of documents and labels based on the Siamese network (Bromley et al., 1993;Hu et al., 2014;Wang and Zhang, 2017), where weight matrices in SLTM represent conditional distributions.
by constraining weight matrices during the learning procedure, SLTM can follow probabilistic characteristics of topic models strictly.