TASK DEFINITION: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
PROBLEM: We create two models both of which constitutes of three main parts: encoder, interaction and classifier and take two sequences as input. The encoder is shared among the sequences simply uses two stacked GRU layers. The interaction part consists of only attention for one model while for the another one it consists of attention and conflict combined as shown in (eqn.11) . The classifier part is simply stacked fully-connected layers. 

SOLUTION: Which neural architecture do they use as a base for their attention conflict mechanisms?

PROBLEM: 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.

SOLUTION: What are three new proposed architectures?

PROBLEM: For the MCTest dataset, Fig. FIGREF30 compares our proposed models with the current state-of-art ensemble of hand-crafted syntactic and frame-semantic features BIBREF16 , as well as past neural models from the literature, all using attention mechanisms — the Attentive Reader of BIBREF26 , Neural Reasoner of BIBREF27 and the HABCNN model family of BIBREF17 . 

SOLUTION:
what is the state of the art for ranking mc test answers?