The central hypothesis of ADAN is that an ideal model for CLSC should learn features that both perform well on sentiment classification for the SOURCE, and are invariant with respect to the shift in language.
as shown in Figure 1, ADAN has a joint feature extractor F that aims to learn features that aid prediction of the sentiment classifier P, and hamper the language discriminator Q, whose goal is to identify whether an input text is from SOURCE or TARGET.