instruction:
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.
question:
The second algorithm incorporates recent ideas of data selection in machine translation BIBREF7 . We develop a novel re-weighting method to resolve these problems, using ideas inspired by data selection in machine translation BIBREF26 , BIBREF7 . 
 Question: What is the data selection paper in machine translation
answer:
BIBREF7 BIBREF26 


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


question:
The second data batch consists of event-related tweets for five natural disasters occurring in the U.S. in 2018. These are: the East Coast Bomb Cyclone (Jan. 2 - 6); the Mendocino, California wildfires (Jul. 27 - Sept. 18); Hurricane Florence (Aug. 31 - Sept. 19); Hurricane Michael (Oct. 7 - 16); and the California Camp Fires (Nov. 8 - 25).  
 Question: Which five natural disasters were examined?
answer:

the East Coast Bomb Cyclone  the Mendocino, California wildfires Hurricane Florence Hurricane Michael the California Camp Fires