Different from (Cheng et al., 2016;Saha et al., 2016) which adopt the fully shared encoder, we share only partial weights for the encoders, considering that 1) The independent weights are devoted to encode the hidden features about the internal characteristics of each corpus, such as the terminology, style, and sentence structure; 2) The shared weights are concentrated on projecting those hidden features into the shared-latent space.
in the proposed model, we apply the self-attention layers to extract the hidden features of the input sentence.