While an extensive study of alternative architectures is beyond the scope of this work, in this section we present a brief experiment illustrating that our adversarial framework works well with other F architectures.
we consider two popular choices: (i) a CNN (Kim, 2014) that has a 1d convolutional layer followed by a single fully-connected layer to extract a fixed-length vector; and (ii) a Bi-LSTM with two variants: one that takes the average of the hidden outputs of each token as the feature vector, and one with the dot attention mechanism (Luong et al., 2015) that learns a weighted linear combination of all hidden outputs.