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]: 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]: We notice that the concatenation consistently outperforms the gated-attention mechanism for both training and testing instructions. We suspect that the gated-attention is useful in the scenarios where objects are described in terms of multiple attributes, but it has no to harming effect when it comes to the order connectors. 
 Question: Why they conclude that the usage of Gated-Attention provides no competitive advantage against concatenation in this setting?
[A]: concatenation consistently outperforms the gated-attention mechanism for both training and testing instructions


[Q]:  In almost all genres, DenseNMT models are significantly better than the baselines. 
 Question: did they outperform previous methods?
[A]:
Yes