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.

Ex 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?

Ex 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


Ex Input:
 We used state-of-the-art features that have shown to be useful in ID: some of them are language-independent (e.g., punctuation marks, positive and negative emoticons, quotations, personal pronouns, tweet's length, named entities) while others are language-dependent relying on dedicated lexicons (e.g., negation, opinion lexicons, opposition words). 
 Question: What text-based features are used?

Ex Output:
language-independent (e.g., punctuation marks, positive and negative emoticons, quotations, personal pronouns, tweet's length, named entities)  language-dependent relying on dedicated lexicons (e.g., negation, opinion lexicons, opposition words)


Ex Input:
Causal documents were chosen to contain one occurrence only of the exact unigrams: `caused', `causing', or `causes'. 
 Question: What are the selection criteria for "causal statements"?

Ex Output:

Presence of only the exact unigrams 'caused', 'causing', or 'causes'