In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Example Input: In the first one, shown in Figure FIGREF12, a special dedicated discriminator is added to the model to control that the latent representation does not contain stylistic information. The second extension of the baseline architecture does not use an adversarial component $D_z$ that is trying to eradicate information on $c$ from component $z$. Instead, the system, shown in Figure FIGREF16 feeds the "soft" generated sentence $\tilde{G}$ into encoder $E$ and checks how close is the representation $E(\tilde{G} )$ to the original representation $z = E(x)$ in terms of the cosine distance. We further refer to it as shifted autoencoder or SAE. We also study a combination of both approaches described above, shown on Figure FIGREF17. 
 Question: What are three new proposed architectures?
Example Output: special dedicated discriminator is added to the model to control that the latent representation does not contain stylistic information shifted autoencoder or SAE combination of both approaches

Example Input: The only remaining question is what makes two environments similar enough to infer the existence of a common category. There is, again, a large literature on this question (including the aforementioned language modeling, unsupervised parsing, and alignment work), but in the current work we will make use of a very simple criterion: fragments are interchangeable if they occur in at least one lexical environment that is exactly the same. 
 Question: How do they determine similar environments for fragments in their data augmentation scheme?
Example Output: fragments are interchangeable if they occur in at least one lexical environment that is exactly the same

Example Input: The memory mechanism is adopted in order to enable the model to look beyond localized features and have access to the entire sequence. 
 Question: Which features do they use?
Example Output:
beyond localized features and have access to the entire sequence