Different from most of existing global HTC methods that rely on pre-specified features (Gopal and Yang, 2013) as input or build on specific models (Cai and Hofmann, 2004;Vens et al., 2008;Silla Jr and Freitas, 2009), our framework is trained in an end-to-end manner by leveraging a differentiable feature representation learning model as the base model.
we use TextCNN (Kim, 2014), HAN (Yang et al., 2016), bow-CNN (Johnson and Zhang, 2014) on the three textual datasets, and a feed-forward network on the two nontextual datasets.