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.

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?
Introduce a "Refinement Adjustment LSTM-based component" to the decoder

Our system, including software and corpus, is available as an open source project for free research purpose and we believe that it is a good baseline for the development and comparison of future Vietnamese SRL systems.  
 Question: Are their corpus and software public?
Yes

The total F-1 score on the OntoNotes dataset is 88%, and the total F-1 cross-validation score on the 112 class Wiki(gold) dataset is 53%.  
 Question: What results do they achieve using their proposed approach?

F-1 score on the OntoNotes is 88%, and it is 53% on Wiki (gold).