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.

[Q]: We compare our model with two sets of baselines:

MemN2N BIBREF12 is an end-to-end trainable version of memory networks BIBREF9 . Attentive and Impatient Readers BIBREF6 use bidirectional LSTMs to encode question and evidence, and do classification over a large vocabulary based on these two encodings. 
 Question: What are the baselines?
[A]: MemN2N BIBREF12 Attentive and Impatient Readers BIBREF6


[Q]: To better analyze model generalization to an unseen, new domain as well as model leveraging the out-of-domain sources, we propose a new architecture which is an extension of the ARED model. In order to better select, aggregate and control the semantic information, a Refinement Adjustment LSTM-based component (RALSTM) is introduced to the decoder side. The proposed model can learn from unaligned data by jointly training the sentence planning and surface realization to produce natural language sentences.  
 Question: What is the difference of the proposed model with a standard RNN encoder-decoder?
[A]: Introduce a "Refinement Adjustment LSTM-based component" to the decoder


[Q]: On the other hand, in the case of MHD, instead of the integration at attention level, we assign multiple decoders for each head and then integrate their outputs to get a final output. 
 Question: Does each attention head in the decoder calculate the same output?
[A]:
No